{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 标注器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import nltk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('and', 'CC'),\n",
       " ('now', 'RB'),\n",
       " ('for', 'IN'),\n",
       " ('something', 'NN'),\n",
       " ('completely', 'RB'),\n",
       " ('different', 'JJ')]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text = nltk.word_tokenize(\"and now for something completely different\")\n",
    "nltk.pos_tag(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "man time day year car moment world house family child country boy\n",
      "state job place way war girl work word\n"
     ]
    }
   ],
   "source": [
    "text = nltk.Text(w.lower() for w in nltk.corpus.brown.words())\n",
    "text.similar('woman')  # 找出和当前词同是名词的词"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "made said done put had seen found given left heard was been brought\n",
      "set got that took in told felt\n"
     ]
    }
   ],
   "source": [
    "text.similar('bought')  # 找出和当前词同是动词的词"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "in on to of and for with from at by that into as up out down through\n",
      "is all about\n"
     ]
    }
   ],
   "source": [
    "text.similar('over')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 标注语料库"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('fly', 'NN')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tagged_token = nltk.tag.str2tuple('fly/NN')\n",
    "tagged_token"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'fly'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tagged_token[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('fly', 'NN'), ('The', 'AT'), ('said', 'VBD')]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = 'fly/NN The/AT said/VBD'\n",
    "[nltk.tag.str2tuple(t) for t in s.split()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('The', 'AT'), ('Fulton', 'NP-TL'), ...]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.corpus.brown.tagged_words()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('The', 'DET'), ('Fulton', 'NOUN'), ...]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# nltk.corpus.brown.tagged_words(simplify_tags=True)  # 部分语料库使用该方式\n",
    "nltk.corpus.brown.tagged_words(tagset='universal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('now', 'RB'), ('im', 'PRP'), ('left', 'VBD'), ...]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.corpus.nps_chat.tagged_words() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Confidence', 'NN'), ('in', 'IN'), ('the', 'DT'), ...]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.corpus.conll2000.tagged_words()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Pierre', 'NNP'), ('Vinken', 'NNP'), (',', ','), ...]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.corpus.treebank.tagged_words()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('一', 'Neu'),\n",
       " ('友情', 'Nad'),\n",
       " ('嘉珍', 'Nba'),\n",
       " ('和', 'Caa'),\n",
       " ('我', 'Nhaa'),\n",
       " ('住在', 'VC1'),\n",
       " ('同一條', 'DM'),\n",
       " ('巷子', 'Nab'),\n",
       " ('我們', 'Nhaa'),\n",
       " ('是', 'V_11')]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.corpus.sinica_treebank.tagged_words()[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.corpus import brown"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FreqDist({'NOUN': 30654, 'VERB': 14399, 'ADP': 12355, '.': 11928, 'DET': 11389, 'ADJ': 6706, 'ADV': 3349, 'CONJ': 2717, 'PRON': 2535, 'PRT': 2264, ...})"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "brown_news_tagged = brown.tagged_words(categories='news', tagset='universal')\n",
    "tag_fd = nltk.FreqDist(tag for (word, tag) in brown_news_tagged)\n",
    "tag_fd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1955c89b190>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tag_fd.plot(cumulative=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 名词\n",
    "N： 普通名词\n",
    "\n",
    "NP：专有名词"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<generator object bigrams at 0x000001955C89E510>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "brown_news_tagged = brown.tagged_words()\n",
    "word_tag_pairs = nltk.bigrams(brown_news_tagged)  # 获取相邻词对\n",
    "word_tag_pairs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(('The', 'AT'), ('Fulton', 'NP-TL')),\n",
       " (('Fulton', 'NP-TL'), ('County', 'NN-TL')),\n",
       " (('County', 'NN-TL'), ('Grand', 'JJ-TL')),\n",
       " (('Grand', 'JJ-TL'), ('Jury', 'NN-TL')),\n",
       " (('Jury', 'NN-TL'), ('said', 'VBD')),\n",
       " (('said', 'VBD'), ('Friday', 'NR')),\n",
       " (('Friday', 'NR'), ('an', 'AT')),\n",
       " (('an', 'AT'), ('investigation', 'NN')),\n",
       " (('investigation', 'NN'), ('of', 'IN')),\n",
       " (('of', 'IN'), (\"Atlanta's\", 'NP$'))]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[(a, b) for (a, b) in word_tag_pairs][:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('JJ-TL', str, 'J'), ('NN-TL', str, 'N'), ('VBD', str, 'V')]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[(b[1], type(b[1]), b[1][0]) for (a, b) in word_tag_pairs][:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['AT', 'JJ', 'IN', 'NN', 'PP$', 'NP', 'CC', ',', '.', 'CD']"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 找出名词一般在哪些词性后面\n",
    "list(nltk.FreqDist(a[1] for (a, b) in word_tag_pairs if b[1][0] == 'N'))[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 找出最频繁的名词\n",
    "def findtags(tag_prefix, tagged_text):\n",
    "    cfd = nltk.ConditionalFreqDist((tag, word) for (word, tag) in tagged_text\n",
    "                                  if tag.startswith(tag_prefix))\n",
    "    return dict((tag, list(cfd[tag].keys())[:5]) for tag in cfd.conditions())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NN ['investigation', 'primary', 'election', 'evidence', 'place']\n",
      "NN$ [\"ordinary's\", \"court's\", \"mayor's\", \"wife's\", \"governor's\"]\n",
      "NN$-HL [\"Golf's\", \"Navy's\"]\n",
      "NN$-TL [\"Department's\", \"Commissioner's\", \"President's\", \"Party's\", \"Mayor's\"]\n",
      "NN-HL ['Merger', 'jail', 'Construction', 'fund', 'sp.']\n",
      "NN-NC ['ova', 'eva', 'aya']\n",
      "NN-TL ['County', 'Jury', 'City', 'Committee', 'Court']\n",
      "NN-TL-HL ['Mayor', 'Commissioner', 'City', 'Oak', 'Grove']\n",
      "NNS ['irregularities', 'presentments', 'thanks', 'reports', 'voters']\n",
      "NNS$ [\"taxpayers'\", \"children's\", \"members'\", \"women's\", \"years'\"]\n",
      "NNS$-HL [\"Dealers'\", \"Idols'\"]\n",
      "NNS$-TL [\"States'\", \"Women's\", \"Princes'\", \"Bombers'\", \"Falcons'\"]\n",
      "NNS-HL ['Wards', 'deputies', 'bonds', 'aspects', 'Decisions']\n",
      "NNS-TL ['Police', 'Roads', 'Legislatures', 'Bankers', 'Reps.']\n",
      "NNS-TL-HL ['Nations']\n"
     ]
    }
   ],
   "source": [
    "tagdict = findtags('NN', nltk.corpus.brown.tagged_words(categories='news'))\n",
    "for tag in sorted(tagdict):\n",
    "    print(tag, tagdict[tag])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 动词"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['is/VERB',\n",
       " 'said/VERB',\n",
       " 'was/VERB',\n",
       " 'are/VERB',\n",
       " 'be/VERB',\n",
       " 'has/VERB',\n",
       " 'have/VERB',\n",
       " 'will/VERB',\n",
       " 'says/VERB',\n",
       " 'would/VERB']"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 新闻文本中最常见的动词\n",
    "wsj = nltk.corpus.treebank.tagged_words(tagset='universal')\n",
    "word_tag_fd = nltk.FreqDist(wsj)\n",
    "[word + \"/\" + tag for (word, tag) in word_tag_fd if tag.startswith('V')][:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['NOUN', 'VERB'])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 给定词的标记的频率顺序列表\n",
    "cfd1 = nltk.ConditionalFreqDist(wsj)\n",
    "cfd1['yield'].keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['will',\n",
       " 'join',\n",
       " 'is',\n",
       " 'publishing',\n",
       " 'was',\n",
       " 'named',\n",
       " 'used',\n",
       " 'make',\n",
       " 'has',\n",
       " 'caused']"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cfd2 = nltk.ConditionalFreqDist((tag, word) for (word, tag) in wsj)\n",
    "list(cfd2['VERB'].keys())[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8232"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp = wsj.index(('kicked', 'VERB'))\n",
    "temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('kicked', 'VERB')"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wsj[temp]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 已标注语料库"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[',',\n",
       " '.',\n",
       " 'accomplished',\n",
       " 'analytically',\n",
       " 'appear',\n",
       " 'apt',\n",
       " 'associated',\n",
       " 'assuming',\n",
       " 'became',\n",
       " 'become',\n",
       " 'been',\n",
       " 'began',\n",
       " 'call',\n",
       " 'called',\n",
       " 'carefully',\n",
       " 'chose',\n",
       " 'classified',\n",
       " 'colorful',\n",
       " 'composed',\n",
       " 'contain',\n",
       " 'differed',\n",
       " 'difficult',\n",
       " 'encountered',\n",
       " 'enough',\n",
       " 'equate',\n",
       " 'extremely',\n",
       " 'found',\n",
       " 'happens',\n",
       " 'have',\n",
       " 'ignored',\n",
       " 'in',\n",
       " 'involved',\n",
       " 'more',\n",
       " 'needed',\n",
       " 'nightly',\n",
       " 'observed',\n",
       " 'of',\n",
       " 'on',\n",
       " 'out',\n",
       " 'quite',\n",
       " 'represent',\n",
       " 'responsible',\n",
       " 'revamped',\n",
       " 'seclude',\n",
       " 'set',\n",
       " 'shortened',\n",
       " 'sing',\n",
       " 'sounded',\n",
       " 'stated',\n",
       " 'still',\n",
       " 'sung',\n",
       " 'supported',\n",
       " 'than',\n",
       " 'to',\n",
       " 'when',\n",
       " 'work']"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看often后面的词\n",
    "brown_learned_text = brown.words(categories='learned')\n",
    "sorted(set(b for (a, b) in nltk.bigrams(brown_learned_text) if a == 'often'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "VERB  ADV  ADP  ADJ    .  PRT \n",
      "  37    8    7    6    4    2 \n"
     ]
    }
   ],
   "source": [
    "brown_lrnd_tagged = brown.tagged_words(categories='learned', tagset='universal')\n",
    "tags = [b[1] for (a, b) in nltk.bigrams(brown_lrnd_tagged) if a[0] == 'often']\n",
    "fd = nltk.FreqDist(tags)\n",
    "fd.tabulate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "combined to achieve\n",
      "continue to place\n",
      "serve to protect\n",
      "wanted to wait\n",
      "allowed to place\n",
      "expected to become\n",
      "expected to approve\n",
      "expected to make\n",
      "intends to make\n",
      "seek to set\n",
      "like to see\n"
     ]
    }
   ],
   "source": [
    "# 使用POS标记寻找三词短语\n",
    "from nltk.corpus import brown\n",
    "def process(sentence):\n",
    "    for (w1, t1), (w2, t2), (w3, t3) in nltk.trigrams(sentence):\n",
    "        if (t1.startswith('V') and t2 == 'TO' and t3.startswith('V')):\n",
    "            print(w1, w2, w3)\n",
    "            \n",
    "for tagged_sent in brown.tagged_sents()[:100]:\n",
    "    process(tagged_sent)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "freq = nltk.defaultdict(int)\n",
    "freq['colorless'] = 4\n",
    "freq['ideas']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pos = nltk.defaultdict(list)\n",
    "pos['sleep'] = ['N', 'V']\n",
    "pos['ideas']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'N'"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "freq = nltk.defaultdict(lambda: 'N')\n",
    "freq['colorless'] = 'ADJ'\n",
    "freq['ideas']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('NN', 13162),\n",
       " ('IN', 10616),\n",
       " ('AT', 8893),\n",
       " ('NP', 6866),\n",
       " (',', 5133),\n",
       " ('NNS', 5066),\n",
       " ('.', 4452),\n",
       " ('JJ', 4392),\n",
       " ('CC', 2664),\n",
       " ('VBD', 2524)]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts = nltk.defaultdict(int)\n",
    "for (word, tag) in brown.tagged_words(categories='news'):\n",
    "    counts[tag] += 1\n",
    "    \n",
    "from operator import itemgetter\n",
    "[(t, c) for t, c in sorted(counts.items(), key=itemgetter(1), reverse=True)][:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 默认标注器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "brown_tagged_sents = brown.tagged_sents(categories='news')\n",
    "brown_sents = brown.sents(categories='news')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'NN'"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tags = [tag for (word, tag) in brown.tagged_words(categories='news')]\n",
    "nltk.FreqDist(tags).max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('i', 'NN'),\n",
       " ('do', 'NN'),\n",
       " ('not', 'NN'),\n",
       " ('like', 'NN'),\n",
       " ('green', 'NN'),\n",
       " ('eggs', 'NN'),\n",
       " ('and', 'NN'),\n",
       " ('ham', 'NN'),\n",
       " (',', 'NN'),\n",
       " ('i', 'NN'),\n",
       " ('do', 'NN'),\n",
       " ('not', 'NN'),\n",
       " ('like', 'NN'),\n",
       " ('them', 'NN'),\n",
       " ('sam', 'NN'),\n",
       " ('i', 'NN'),\n",
       " ('am', 'NN'),\n",
       " ('!', 'NN')]"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw = 'i do not like green eggs and ham, i do not like them sam i am!'\n",
    "tokens = nltk.word_tokenize(raw)\n",
    "default_tagger = nltk.DefaultTagger('NN')\n",
    "default_tagger.tag(tokens)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.13089484257215028"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "default_tagger.evaluate(brown_tagged_sents)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 正则表达式标注器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('``', 'NN'),\n",
       " ('Only', 'NN'),\n",
       " ('a', 'NN'),\n",
       " ('relative', 'NN'),\n",
       " ('handful', 'NN'),\n",
       " ('of', 'NN'),\n",
       " ('such', 'NN'),\n",
       " ('reports', 'NNS'),\n",
       " ('was', 'NNS'),\n",
       " ('received', 'VBD'),\n",
       " (\"''\", 'NN'),\n",
       " (',', 'NN'),\n",
       " ('the', 'NN'),\n",
       " ('jury', 'NN'),\n",
       " ('said', 'NN'),\n",
       " (',', 'NN'),\n",
       " ('``', 'NN'),\n",
       " ('considering', 'VBG'),\n",
       " ('the', 'NN'),\n",
       " ('widespread', 'NN'),\n",
       " ('interest', 'NN'),\n",
       " ('in', 'NN'),\n",
       " ('the', 'NN'),\n",
       " ('election', 'NN'),\n",
       " (',', 'NN'),\n",
       " ('the', 'NN'),\n",
       " ('number', 'NN'),\n",
       " ('of', 'NN'),\n",
       " ('voters', 'NNS'),\n",
       " ('and', 'NN'),\n",
       " ('the', 'NN'),\n",
       " ('size', 'NN'),\n",
       " ('of', 'NN'),\n",
       " ('this', 'NNS'),\n",
       " ('city', 'NN'),\n",
       " (\"''\", 'NN'),\n",
       " ('.', 'NN')]"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patterns = [\n",
    "    (r'.*ing$', 'VBG'),\n",
    "    (r'.*ed$', 'VBD'),\n",
    "    (r'.*es$', 'VBZ'),\n",
    "    (r'.*ould$', 'MD'),\n",
    "    (r'.*\\'s$', 'NN$'),\n",
    "    (r'.*s$', 'NNS'),\n",
    "    (r'^-?[0-9]+(.[0-9]+)?$', 'CD'),\n",
    "    (r'.*', 'NN')\n",
    "]\n",
    "\n",
    "regexp_tagger = nltk.RegexpTagger(patterns)\n",
    "regexp_tagger.tag(brown_sents[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.20326391789486245"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regexp_tagger.evaluate(brown_tagged_sents)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 查询标注器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3329355371243312"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fd = nltk.FreqDist(brown.words(categories='news'))  # 统计词频\n",
    "cfd = nltk.ConditionalFreqDist(brown.tagged_words(categories='news'))  # 词 - 词性 统计\n",
    "\n",
    "most_freq_words = list(fd.keys())[:100]  # 最高频的前100词\n",
    "likely_tags = dict((word, cfd[word].max()) for word in most_freq_words)  # 高频词及高频词性\n",
    "\n",
    "baseline_tagger = nltk.UnigramTagger(model=likely_tags)\n",
    "\n",
    "baseline_tagger.evaluate(brown_tagged_sents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('``', '``'),\n",
       " ('Only', 'RB'),\n",
       " ('a', 'AT'),\n",
       " ('relative', 'JJ'),\n",
       " ('handful', 'NN'),\n",
       " ('of', 'IN'),\n",
       " ('such', 'JJ'),\n",
       " ('reports', 'NNS'),\n",
       " ('was', 'BEDZ'),\n",
       " ('received', 'VBD'),\n",
       " (\"''\", \"''\"),\n",
       " (',', ','),\n",
       " ('the', 'AT'),\n",
       " ('jury', 'NN'),\n",
       " ('said', 'VBD'),\n",
       " (',', ','),\n",
       " ('``', '``'),\n",
       " ('considering', 'IN'),\n",
       " ('the', 'AT'),\n",
       " ('widespread', 'JJ'),\n",
       " ('interest', 'NN'),\n",
       " ('in', 'IN'),\n",
       " ('the', 'AT'),\n",
       " ('election', 'NN'),\n",
       " (',', ','),\n",
       " ('the', 'AT'),\n",
       " ('number', 'NN'),\n",
       " ('of', 'IN'),\n",
       " ('voters', 'NNS'),\n",
       " ('and', 'CC'),\n",
       " ('the', 'AT'),\n",
       " ('size', 'NN'),\n",
       " ('of', 'IN'),\n",
       " ('this', 'DT'),\n",
       " ('city', 'NN'),\n",
       " (\"''\", \"''\"),\n",
       " ('.', '.')]"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sent = brown.sents(categories='news')[3]\n",
    "baseline_tagger.tag(sent)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 从上可知有些词被标注为空，因为我们需要将未查到的词性标注为默认的\n",
    "baseline_tagger = nltk.UnigramTagger(model=likely_tags, backoff=nltk.DefaultTagger('NN'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "def performance(cfd, wordlist):\n",
    "    lt = dict((word, cfd[word].max()) for word in wordlist)\n",
    "    baseline_tagger = nltk.UnigramTagger(model=lt,\n",
    "                                        backoff=nltk.DefaultTagger('NN'))\n",
    "    return baseline_tagger.evaluate(brown.tagged_sents(categories='news'))\n",
    "\n",
    "def display():\n",
    "    import pylab\n",
    "    words_by_freq = list(nltk.FreqDist(brown.words(categories='news')))\n",
    "    cfd = nltk.ConditionalFreqDist(brown.tagged_words(categories='news'))\n",
    "    sizes = 2 ** pylab.arange(15)\n",
    "    perfs = [performance(cfd, words_by_freq[:size]) for size in sizes]\n",
    "    pylab.plot(sizes, perfs, '-bo')\n",
    "    pylab.title('Lookup Tagger Performance with Varying Model Size')\n",
    "    pylab.xlabel('Model Size')\n",
    "    pylab.ylabel('Performance')\n",
    "    pylab.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "display()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# N-gram标注"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 一元标注\n",
    "基于简单的统计算法，对每个标识符分配这个独特的标识符最有可能的标记\n",
    "\n",
    "即对每一个词统计频率最高的词性频率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Various', 'JJ'),\n",
       " ('of', 'IN'),\n",
       " ('the', 'AT'),\n",
       " ('apartments', 'NNS'),\n",
       " ('are', 'BER'),\n",
       " ('of', 'IN'),\n",
       " ('the', 'AT'),\n",
       " ('terrace', 'NN'),\n",
       " ('type', 'NN'),\n",
       " (',', ','),\n",
       " ('being', 'BEG'),\n",
       " ('on', 'IN'),\n",
       " ('the', 'AT'),\n",
       " ('ground', 'NN'),\n",
       " ('floor', 'NN'),\n",
       " ('so', 'QL'),\n",
       " ('that', 'CS'),\n",
       " ('entrance', 'NN'),\n",
       " ('is', 'BEZ'),\n",
       " ('direct', 'JJ'),\n",
       " ('.', '.')]"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "brown_tagged_sents = brown.tagged_sents(categories='news')\n",
    "brown_sents = brown.sents(categories='news')\n",
    "\n",
    "unigram_tagger = nltk.UnigramTagger(brown_tagged_sents)  # 一元标注\n",
    "unigram_tagger.tag(brown_sents[2007])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9349006503968017"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unigram_tagger.evaluate(brown_tagged_sents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4160"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 分离训练和测试数据\n",
    "size = int(len(brown_tagged_sents) * 0.9)\n",
    "size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8121200039868434"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_sents = brown_tagged_sents[:size]\n",
    "test_sents = brown_tagged_sents[size:]\n",
    "\n",
    "unigram_tagger = nltk.UnigramTagger(train_sents)\n",
    "\n",
    "unigram_tagger.evaluate(test_sents)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## n-gram标注器\n",
    "挑选在给定上下文中最可能的标记\n",
    "\n",
    "上下文：当前词和它前面的n-1个标识符的词性标记（不包含前一个句子）\n",
    "\n",
    "当标注的数据在训练数据中不存在且占比较高时，被称为数据稀疏问题，在NLP中是普遍的。因此在研究结果的精度和覆盖范围之间需要有一个权衡"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Various', 'JJ'),\n",
       " ('of', 'IN'),\n",
       " ('the', 'AT'),\n",
       " ('apartments', 'NNS'),\n",
       " ('are', 'BER'),\n",
       " ('of', 'IN'),\n",
       " ('the', 'AT'),\n",
       " ('terrace', 'NN'),\n",
       " ('type', 'NN'),\n",
       " (',', ','),\n",
       " ('being', 'BEG'),\n",
       " ('on', 'IN'),\n",
       " ('the', 'AT'),\n",
       " ('ground', 'NN'),\n",
       " ('floor', 'NN'),\n",
       " ('so', 'CS'),\n",
       " ('that', 'CS'),\n",
       " ('entrance', 'NN'),\n",
       " ('is', 'BEZ'),\n",
       " ('direct', 'JJ'),\n",
       " ('.', '.')]"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bigram_tagger = nltk.BigramTagger(train_sents)  # 二元标注（未出现的不会统计）\n",
    "bigram_tagger.tag(brown_sents[2007])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('The', 'AT'),\n",
       " ('population', 'NN'),\n",
       " ('of', 'IN'),\n",
       " ('the', 'AT'),\n",
       " ('Congo', 'NP'),\n",
       " ('is', 'BEZ'),\n",
       " ('13.5', None),\n",
       " ('million', None),\n",
       " (',', None),\n",
       " ('divided', None),\n",
       " ('into', None),\n",
       " ('at', None),\n",
       " ('least', None),\n",
       " ('seven', None),\n",
       " ('major', None),\n",
       " ('``', None),\n",
       " ('culture', None),\n",
       " ('clusters', None),\n",
       " (\"''\", None),\n",
       " ('and', None),\n",
       " ('innumerable', None),\n",
       " ('tribes', None),\n",
       " ('speaking', None),\n",
       " ('400', None),\n",
       " ('separate', None),\n",
       " ('dialects', None),\n",
       " ('.', None)]"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unseen_sent = brown_sents[4203]\n",
    "bigram_tagger.tag(unseen_sent)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.10206319146815508"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bigram_tagger.evaluate(test_sents)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 组合标注器\n",
    "如首先使用二元标注器，否则使用一元标注器，最后使用默认标注器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8452108043456593"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t0 = nltk.DefaultTagger('NN')\n",
    "t1 = nltk.UnigramTagger(train_sents, backoff=t0)\n",
    "t2 = nltk.BigramTagger(train_sents, backoff=t1)\n",
    "t2.evaluate(test_sents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8424200139539519"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 将会丢弃那些只看到一次或两次的上下文\n",
    "t2 = nltk.BigramTagger(train_sents, cutoff=2, backoff=t1)\n",
    "t2.evaluate(test_sents)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 存储标注器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pickle import dump\n",
    "from pickle import load"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "output = open('t2.pkl', 'wb')\n",
    "dump(t2, output, -1)\n",
    "output.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs = open('t2.pkl', 'rb')\n",
    "tagger = load(inputs)\n",
    "inputs.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('One', 'CD'),\n",
       " ('notable', 'JJ'),\n",
       " ('effort', 'NN'),\n",
       " ('in', 'IN'),\n",
       " ('increasing', 'VBG'),\n",
       " ('the', 'AT'),\n",
       " ('interoperability', 'NN'),\n",
       " ('of', 'IN'),\n",
       " ('biomedical', 'NN'),\n",
       " ('ontologies', 'NN'),\n",
       " ('has', 'HVZ'),\n",
       " ('been', 'BEN'),\n",
       " ('the', 'AT'),\n",
       " ('creation', 'NN'),\n",
       " ('of', 'IN'),\n",
       " ('logical', 'JJ'),\n",
       " ('definitions[71].', 'NN'),\n",
       " ('This', 'DT'),\n",
       " ('is', 'BEZ'),\n",
       " ('an', 'AT'),\n",
       " ('initiative', 'NN')]"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text = '''\n",
    "One notable effort in increasing the interoperability of biomedical ontologies has been the creation of logical definitions[71]. This is an initiative \n",
    "'''\n",
    "tokens = text.split()\n",
    "tagger.tag(tokens)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 性能限制"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('NN', \"''\", 'that'),\n",
       " ('NN', 'NNS', 'that'),\n",
       " ('AT', 'NN-TL', 'of'),\n",
       " ('NP', 'NP', 'to'),\n",
       " ('NN', 'IN', 'such'),\n",
       " ('NN', 'VBD', 'it'),\n",
       " ('VB', '``', 'to'),\n",
       " ('CC', 'VBN', 'to'),\n",
       " ('NN', 'VBD', 'on'),\n",
       " ('WDT', 'VB', 'to')]"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cfd = nltk.ConditionalFreqDist(((x[1], y[1], z[0]), z[1])\n",
    "                              for sent in brown_tagged_sents\n",
    "                              for x, y, z in nltk.trigrams(sent))\n",
    "\n",
    "ambiguout_contexts = [c for c in cfd.conditions() if len(cfd[c]) > 1]\n",
    "ambiguout_contexts[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.049297702068029296"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(cfd[c].N() for c in ambiguout_contexts) / cfd.N()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可知有1/20的trigrams是有歧义的。给定当前单词及其前两个标记，根据训练数据，在5%的情况下，有一个以上的标记可能合理的分配给当前词。假设我们总是挑选在这种含糊不清的上下文中最可能的标记，可以得出trigram标注器性能的下界\n",
    "\n",
    "训练数据中的歧义导致标注器性能的上限"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 另一种查看标注错误的方法：查看混淆矩阵\n",
    "test_tags = [tag for sent in brown.sents(categories='editorial')\n",
    "            for (word, tag) in t2.tag(sent)]\n",
    "gold_tags = [tag for (word, tag) in brown.tagged_words(categories='editorial')]\n",
    "\n",
    "t = nltk.ConfusionMatrix(gold_tags, test_tags)  # 混淆矩阵"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # 查看与写入文件\n",
    "# print(str(t))\n",
    "# f = open('test.txt', 'w')\n",
    "# l = [w[:100] for w in str(t).split('\\n')]\n",
    "# [f.write(ls+\"\\n\") for ls in l]\n",
    "# f.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 跨句子边界标注\n",
    "对n-gram标注器用于句子的第一个词时，通常是前面句子的最后一个词或标点符号，这对于该词的类别是没用关系的，因此使用跨句子边界标注方式"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8452108043456593"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "brown_tagged_sents = brown.tagged_sents(categories='news')\n",
    "brown_sents = brown.sents(categories='news')\n",
    "\n",
    "size = int(len(brown_tagged_sents) * 0.9)\n",
    "train_sents = brown_tagged_sents[:size]\n",
    "test_sents = brown_tagged_sents[size:]\n",
    "\n",
    "t0 = nltk.DefaultTagger('NN')\n",
    "t1 = nltk.UnigramTagger(train_sents, backoff=t0)\n",
    "t2 = nltk.BigramTagger(train_sents, backoff=t1)\n",
    "\n",
    "t2.evaluate(test_sents)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 基于转换的标注\n",
    "## Brill标注\n",
    "思想：猜每个词的标记，然后返回和修复错误。这种方式下，Brill标注器陆续将一个不良标注的文本转换成一个更好的。同样是有监督的学习方法，不过和n-gram不同的是，它不计数观察结果，只编制一个转换修正规则的链表。\n",
    "\n",
    "以画画类比：以大笔画开始，然后修复细节，一点点的修改"
   ]
  },
  {
   "attachments": {
    "%E5%9B%BE%E7%89%87.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![%E5%9B%BE%E7%89%87.png](attachment:%E5%9B%BE%E7%89%87.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading tagged data from treebank... \n",
      "Read testing data (200 sents/5251 wds)\n",
      "Read training data (800 sents/19933 wds)\n",
      "Read baseline data (800 sents/19933 wds) [reused the training set]\n",
      "Trained baseline tagger\n",
      "    Accuracy on test set: 0.8366\n",
      "Training tbl tagger...\n",
      "TBL train (fast) (seqs: 800; tokens: 19933; tpls: 24; min score: 3; min acc: None)\n",
      "Finding initial useful rules...\n",
      "    Found 12799 useful rules.\n",
      "\n",
      "           B      |\n",
      "   S   F   r   O  |        Score = Fixed - Broken\n",
      "   c   i   o   t  |  R     Fixed = num tags changed incorrect -> correct\n",
      "   o   x   k   h  |  u     Broken = num tags changed correct -> incorrect\n",
      "   r   e   e   e  |  l     Other = num tags changed incorrect -> incorrect\n",
      "   e   d   n   r  |  e\n",
      "------------------+-------------------------------------------------------\n",
      "  23  23   0   0  | POS->VBZ if Pos:PRP@[-2,-1]\n",
      "  18  19   1   0  | NN->VB if Pos:-NONE-@[-2] & Pos:TO@[-1]\n",
      "  14  14   0   0  | VBP->VB if Pos:MD@[-2,-1]\n",
      "  12  12   0   0  | VBP->VB if Pos:TO@[-1]\n",
      "  11  11   0   0  | VBD->VBN if Pos:VBD@[-1]\n",
      "  11  11   0   0  | IN->WDT if Pos:-NONE-@[1] & Pos:VBP@[2]\n",
      "  10  11   1   0  | VBN->VBD if Pos:PRP@[-1]\n",
      "   9  10   1   0  | VBD->VBN if Pos:VBZ@[-1]\n",
      "   8   8   0   0  | NN->VB if Pos:MD@[-1]\n",
      "   7   7   0   1  | VB->NN if Pos:DT@[-1]\n",
      "   7   7   0   0  | VB->VBP if Pos:PRP@[-1]\n",
      "   7   7   0   0  | IN->WDT if Pos:-NONE-@[1] & Pos:VBZ@[2]\n",
      "   7   8   1   0  | IN->RB if Word:as@[2]\n",
      "   6   6   0   0  | VBD->VBN if Pos:VBP@[-2,-1]\n",
      "   6   6   0   1  | IN->WDT if Pos:-NONE-@[1] & Pos:VBD@[2]\n",
      "   5   5   0   0  | POS->VBZ if Pos:-NONE-@[-1]\n",
      "   5   5   0   0  | VB->VBP if Pos:NNS@[-1]\n",
      "   5   5   0   0  | VBD->VBN if Word:be@[-2,-1]\n",
      "   4   4   0   0  | POS->VBZ if Pos:``@[-2]\n",
      "   4   4   0   0  | VBP->VB if Pos:VBD@[-2,-1]\n",
      "   4   6   2   3  | RP->RB if Pos:CD@[1,2]\n",
      "   4   4   0   0  | RB->JJ if Pos:DT@[-1] & Pos:NN@[1]\n",
      "   4   4   0   0  | NN->VBP if Pos:NNS@[-2] & Pos:RB@[-1]\n",
      "   4   5   1   0  | VBN->VBD if Pos:NNP@[-2] & Pos:NNP@[-1]\n",
      "   4   4   0   0  | IN->WDT if Pos:-NONE-@[1] & Pos:MD@[2]\n",
      "   4   8   4   0  | VBD->VBN if Word:*@[1]\n",
      "   4   4   0   0  | JJS->RBS if Word:most@[0] & Word:the@[-1] & Pos:DT@[-1]\n",
      "   3   3   0   0  | VBD->VBN if Pos:VBN@[-1]\n",
      "   3   4   1   0  | VBN->VB if Pos:TO@[-1]\n",
      "   3   4   1   1  | IN->RB if Pos:.@[1]\n",
      "   3   3   0   0  | JJ->RB if Pos:VBD@[1]\n",
      "   3   3   0   0  | PRP$->PRP if Pos:TO@[1]\n",
      "   3   3   0   0  | NN->VBP if Pos:NNS@[-1] & Pos:DT@[1]\n",
      "   3   3   0   0  | VBP->VB if Word:n't@[-2,-1]\n",
      "Trained tbl tagger in 2.48 seconds\n",
      "    Accuracy on test set: 0.8572\n",
      "Tagging the test data\n"
     ]
    }
   ],
   "source": [
    "from nltk.tbl import demo as brill\n",
    "brill.demo()"
   ]
  },
  {
   "attachments": {
    "%E5%9B%BE%E7%89%87.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 确定词性\n",
    "## 形态学线索\n",
    "![%E5%9B%BE%E7%89%87.png](attachment:%E5%9B%BE%E7%89%87.png)"
   ]
  },
  {
   "attachments": {
    "%E5%9B%BE%E7%89%87.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 句法线索\n",
    "![%E5%9B%BE%E7%89%87.png](attachment:%E5%9B%BE%E7%89%87.png)"
   ]
  },
  {
   "attachments": {
    "%E5%9B%BE%E7%89%87.png": {
     "image/png": 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LC1FdXd3peyhy69YtPH36tEvXaEpVVVWH/1f3u1dVVXHS42UiFAohEole+nNeRL135coVNDU1dem5MplMZXpWVFRw7qXJu6j73TqrE9XxItKlu3S3XigrK9PovH+ciPntt98QHR0NPp/P2sLCwvDpp5+qFDKqfvDm5makp6fDzc0N+vr6GDp0KGpra1/FJ3SIVCrFF198gWPHjnV67tGjR/HkyRMAQH5+Pi5evAiBQMDaPD094efnx7nWyckJ27dvZx3btWsXVqxYofG7Ojk5se4tFApVVsZisRi2trbkt4qNjUVkZCTZ37ZtG86ePavxcxlmz56NjIwMtf+fPHky8vPzyf7FixdhYmICQLVQ6E56ymQybNq0CUVFRQDaG0lV+a2lpQXFxcUq3zMxMRHr168n+zNmzEBFRQW5f01NjdpvVGzco6KisHDhQrXnAu0Ff+7cuZzys3z5cjg5OXV4bU/oSr5Wvu4///kPrl27xvmfsrDZvn07fHx8yDcFBgbC09OT7MfHx+OTTz5h5QmG7pQHqVSKUaNGsYRCUFAQLCwskJqaihs3bmj0jYcPH4a/vz/Zl8lkXRZCqmhpacGHH37IETL19fXkbx8fHxw9epRzrbe3d6d56UWyYsUKWFhYaHTu31nvtbW1YdCgQQgJCen03Lq6OsjlciQlJWHixInYvHkzSktLIZPJkJKSAmtra/zwww/YtWsX67pHjx5hwYIFnDKquJmYmCA9PZ3zzKlTpyI2Nlajb1HkRbQH3aG79QIA2NjYIC4urtPz/nEiRkdHBzk5OcjPz2dtOTk50NXVVXlNXl4eTExMcO7cOfj7++PEiRPw8PBATEwMcnNzX7rVoyucOnUKy5cvx6lTpzqtyHJycjBy5EgAgEgkUvkdHh4eOHHiBOe4tbU1WlpaWA3BuHHj8PjxY5XPkslkCAkJAZ/Px86dOxEQEIDTp0/Dx8cHa9asgZeXF3x8fBAUFMS5Vi6X46effiL7aWlpsLGxIfv29va4ePEi61lXr15FcnIy2WxsbJCbm8u6r46ODuebk5KSEBUVBT6fjx9//JEleB0dHWFqago+nw9DQ0NO5d3d9MzIyMD06dMBANeuXcPy5cs5laq/vz/c3d3J9wUFBSEuLg58Ph8uLi5YtmwZec8xY8aQv93d3bF161bIZDIAwOLFixEeHo5jx47ByckJe/bswdWrVyEQCGBnZ4c1a9ZAIBAgNjYWK1eu5Ii1yspKzJkzh/Nd27ZtQ0pKCuf4i0KTfF1fX4+srCzW5unpCV1dXc5xd3d3TJkyhdVp2bJlC+sbBAIBXF1dWc/4+eefVT67q+UBAEJCQuDg4MA6FhkZib1790IsFmPixIkc8cX89gkJCSRv/P7779i9ezfZ9/Hxgb29PfnNu0tsbCysra2RlJSEs2fPwtHREVFRUViyZAnpsAUGBuLMmTOca7du3Yro6OgePV9TCgoKMHDgQMTFxbHK/O7du1U21K+y3lN1XxsbGzg6OnZqHRwzZgz8/PwgEAhIepuamuKPP/5Adna2WotrYWEhFixYwDo2c+ZMlpVlw4YNHDFeUFCA0aNHQywWd/heqt67p+nSXbrS3t2+fZu1f+/ePfz555+sY3fv3sXz589Zx/5xImbGjBlqRcyMGTNUXvPo0SMYGhq+4jftOsXFxRg2bBgZXli1alWnJlblIROGkpIStLW1wcPDA5GRkeR4QkICHBwccOTIEQgEAhgYGCApKQk5OTmYOnUqaTxdXFw4ypwhNzcXa9euJfvz5s1jZTCml6TIhAkTyN+qRIxAIGCdX1paCpFIhJaWFohEInz66acoLS1lnTNz5kw0NjayMr9iAdbR0WEN0yQnJ8POzk7lNzF0JT0rKiqI1eX+/ftITU3F1atXO32GMikpKTh06BDZVyUyGPT19fHgwQPY29sjODgYkZGRxAzs6uqKsLAwAFDbANbU1GDcuHEckWViYvLSRIym+VosFuPOnTuoqqpCVVUVhEIhRo4cibt375JjyptiQ7B9+/Yui5julge5XA5jY2O0tbWhvLwcAQEB5H5Mz/r58+cIDAzEs2fPyHXV1dUcs73i78ZQW1vLqYy7ytKlS/Hw4UMA7RYiRkQDwNmzZxEfH4/g4GCVImbWrFnEApidnQ0ARCALBALs2LED+/fv79H7Ae3pOGfOHGRkZKClpYVseXl5GDRoEKdeYPg76r2ysjKMGjUKDQ0NuHPnjtrzGBihpcjixYuRmZnZ4XVPnjyBkZERJBIJGhsb0djYSOq606dPQyKRqBQxNjY2pC5NSEiArq4u7t27h+XLl5Pv8/DwgLGx8QtNl+7Slfaura0NxsbGHVqn+Hw+xo4dC2dnZ9a1/zgRo62tjZiYGE4lHBMTg6lTpwLgKs38/PxORYxAIMCVK1de2nt3hkgkwsyZM5GTk4ObN2/i+fPnePDgAfT09FBeXq72usbGRkgkEpw5c4bVu/vzzz+RkpLCKczx8fGsBvPXX39FW1sb1q9fT4YwAOD48eM4cuQIgPahF39/f8THx5PMcuLECfD5fJw5cwZ+fn7k+OnTp6Gvr88Z1vvuu+/Iu7m7u8PIyIjsL1q0iFNZ3blzh3x3eno6zMzMcPbsWcTGxpJnjRo1CqGhoVi+fDmroWDQ0dHBrVu3kJubi9zcXJw+fbpTgdGV9KypqcG8efPIUFJkZCQyMzM1FjHp6emIjY2FUChEcXExKcDM/VT5M8ydO5cjYlJSUsDn82FgYIA///wTfD4fW7ZsUTmc8fz5c7WWmJfh49TdfA0AR44cwfHjx1nH2tra1L6nnZ0dvLy8kJaWhrS0NLi7u8Pa2prsp6WlYcyYMaxrulMegHZRkJSUBKDdAuTg4IDz58/DxcUFJiYmSEhIQHx8PAICAnD37l1y3alTp1jlhc/nY/bs2fD19WUds7a2hp6enka+QKqoqanB//73P/LslStXori4GEKhEBkZGXBzc8PVq1dZIqauro6823fffUfe5bvvvoNYLIaOjg65v4eHB0d4dQcHBwdYWVmRtATa88z48eMRFRWl9rpXUe8pUlFRgalTp+LOnTvk2LFjx7Bu3Tq1De+IESNIGhoaGuLGjRsqRYzyMHNxcTGMjY1RW1uLiIgIpKSkYMKECUhJSUFKSgpqa2s5Iqa2thbr16+HRCLBxIkTIZFIIJFIIJPJiJUY4HaYepou3aWr9cLRo0dhbW3d4T2FQqHKPPOPEDGtra3kBzx+/Dj5W3G7cuUKIiIiYGtri3nz5rF6aI8fP8aUKVM4wkd5S0tLeyXfwufzWcfq6+thZmaG69evAwCcnZ1Jby4tLQ1ffPEFHB0dSc+srq4O/v7+OHLkCJYvXw6A7Qwml8tJo66qR6KcaW/cuME6B2jPNKdOneK8f0xMDMe0n5WVheTkZAQHB6v97vHjx5O/NbHEAO091Lq6OuzcuRPR0dEcJzNdXd0OhwJ1dHRQUFCA0tJSlJaWIjQ0VKXA6El6njt3Djt37kR1dTViY2O7JGIAwMDAAA8ePMCdO3egq6vLasgmTpyIxMRE1vlz585FWloatmzZgpMnT7LeZdq0aSwLjKLvA4NQKFRriVFsSLrKi8jXiqSmpuKjjz5CYmIi6z2DgoIwZMgQlb/7jh07cPnyZdJ7jYmJgZOTE9lvbGxkWQSB7pWH58+f47PPPsORI0ewd+9elk/D7du3WZVtVVUVcdwUi8UYMmQIsXBIpVLY2Nhg69atxKJ48uRJACDHusvRo0fx008/oaioCFKpFAsWLIBAIMC2bduwdu1aUo+qssSUlJRg8eLFANotokuXLgUA1nC9cjnoDhEREYiIiIBcLsesWbMQGxuL1tZW6OnpwdfXl3P+31XvpaWlwdjYGI8ePWIdr6ysxKFDhzB06FAcPnwY2dnZLNGpKJjNzc1RVVUFY2NjeHh4QCAQYOfOnfD19cW6detYHZbCwkKYmpqyLI4zZ85EVVUV0tLSEBoayhExXl5eqKurAwBMmTKF9Z6KIxTKIqan7UFnvKh6oaWlhdWBZXzcFDvWe/bswaJFizhW6H+EiAHaTWyMamRMjjNmzGCZIFtaWlBRUYGWlhbWhxQUFMDMzOylvZtIJIKfnx8rQdVtvr6+2LFjBxkjvXnzJuzs7FiWBOUMU1xcDF1dXfz888+sMUqBQIDDhw8DaBcXmzdvBp/Ph7OzM4yMjABwC7NAIOBk2ps3byIvLw8RERHkuLLTLsPkyZMhFAo5W0pKCstMqUxnPjHx8fGcaxobG7Fy5UpMmjSJmFY3bdpEKgpNRExXhpO6k55yuRwikQinTp1Cc3OzxiImMDAQFRUVEAqFSE9PR35+Pseny8jIiDObysDAAOHh4Vi5ciUOHjwIT09PRERE4PTp05g5cyZxaD1+/Dg2bNjA8eCvr69X6WdQUlICuVwOX19fXLp0CcDfl6+vX7+OzZs3Y+zYsZz3FAqFaq2qW7duRVZWFtnXZDipu+WhrKwM9fX1sLS0hFQqhUgkQmFhIcLDw6Grq4uEhATiP8YM47S2tsLNzQ0ymQwCgQA2Nja4e/cuxGIx7t69C21tbTg4OEAkEvVopmV9fT2OHz8OS0tLFBUVITo6mrz7xo0bce3atQ5FTHh4ODw9PQEAfD6flIkXKWJqampYQ0IFBQXIy8uDrq5up06zr6req6mpgZ+fH8LDw+Hk5MQZpggMDMTixYtx7949mJiYYM+ePazhbMXhJEbEzJw5kwy3W1hYqBwWy8nJwbJly5CYmIgbN24gMzOTtWVnZ3NEDGO9BdqH2QEgLCwMcrmc7ANcEdOddPm76gXlzo6vr69GwuofI2KA9gRfsWIFSaCZM2ciMjISJ0+eJD21L7/8kmOeu3nzJn7//XfU19dj8+bNnF7oyZMnsW/fvlf+PU+ePGGZmhlcXV1V/jjKlgjFwpyQkED+Tk5OJhlTuTAnJSXBwsKCfPuECRPIfefOnYubN28CAPbt26fSsjJ58mSVVqzTp09jw4YNKr+zra0NQ4cOJedeuHAB/v7+ZH/x4sUqRQwA+Pn5Yd68eWR/3bp18Pb2BqCZiImPjyfPcXFx0VjEaJqeDHv27AEAjUVMRUUFaaQZ5zRlEfPrr79yvs/Q0JA1nBQeHg6gPV2YAp+VlQVDQ0PWLKmIiAjEx8cjLi4OcXFxEAgE8PDwwNKlS1m/Y0hICC5cuNDp+3dEd/O1XC5HWFgYzp07B4Btjme2s2fPqhUxa9euJT4ggGYiprvlQSKRYOvWraipqYFMJkNERARycnJQWlpKzPdFRUUqZ0LJ5XJMmzYNv//+O9zc3DB+/HhYW1ujrq4Oly9f7rFDb2FhIWQyGRExBw4cgI2NDe7du4cpU6ZAKpV2KGI2b95MnOgdHByIlbQjEVNVVYXhw4fD3t6+y+/b0NCAa9euQV9fH7du3WL9T5Wz599R761Zs4YzE8bd3R3z589X+12KQ+g6OjooLy/H0KFDSSdMnYj566+/sHXrVgCAra0tsUaePXsWO3bsAMB17I2IiEBMTAz4fD5Gjx6NmJgYeHh44MmTJ6zfTVnE9DRdukJP2zsAcHFxIW3FaytirKysyBAS8+MwM2KuX78OKysrznXx8fFwdHRES0sLx9QGtCtWpiAoUltb+0piFyij7kdVRrEwJyUlsQozM2tCuTBfvnxZ5Rgo0D6t8vz58wDa/QtUNdaKY7OKW1BQEMvZVxGmp1lYWAig3TFYceyyoaFB5Zx/mUwGAwMDzJ8/n5geL1y4wOoZdtcSI5FISAFl6E56Au1xI5iGtyvDSQUFBcjOziY9HmURM2nSJI5PhLJPTGhoKLKysjB//nwiTk6dOoW5c+dixYoVnBgUFy9exPjx4yGVSjkWMalUqrLRfVF0lq+FQiHr91Qe+mHOUSdijI2NERERQQSPg4MDLC0tWSJo9OjRrGu6Wx7mzp2LlStXwtXVFZGRkazGaOTIkWhtbcW0adNY6auITCZDVIQb6xYAACAASURBVFQUduzYwfKzyMrKwk8//UTye0+wtLSEQCBAZWUlsrKycOrUKWzevBlAuz+Hsk8Mw6xZs0i+MzAwIMOSHYmYiooKDB48GDt37uzSO9bW1sLCwgLjxo3j+DomJibi888/Z5Vh4O+p93R0dDi/yfr162Ftba3SaiYWi1kWkOjoaDx48IA160idiPHz8yPDigYGBqQxz8rKInWLKsdeoN1K8tNPP7GEsLIl5uDBgy8sXV4EmrZ3QHvevHz5MoB2EbNp0yYIBALEx8dj/PjxrLLE8I8TMQcPHiTj20yhio6ORkZGBgwMDFTOjHF0dMTly5fR1tamVsQo99ikUinee+89jiPgq6CrIubRo0fw8fHBqlWriMWBsSxpYlZlKgnFBnPjxo1ISEhgPU8ul8PJyQlXr16FUCjE3r17kZ+fD6FQiNzcXLU9eGZ828zMjPTqDA0NSQGpqKjAzp07yTAGg5ubG+Lj45GamkqctqVSKbEwKIuY4uJinD17llSCS5cuZcWQ8Pb2xs6dOyEQCHD06FHMmTOHNVzT1fSsrq6Gu7s7hg0bRt5DUxFTXV2N+vp6GBsbk0qKyc85OTkwMTHBtGnTAIBVMLW1tREYGIgNGzbAzs4Onp6e2Lp1KxoaGkh65ubmqm08N2/ejNDQUADtw3qKDtbBwcE4dOjQSwtw1ZXKCgCGDx/OsfiFhoaqFTFz585l7SckJHRrOEmT8hAbG0sqU2UsLCwwfvx4uLi4qPz/jRs38H//93/Q1tbG7t27YWVlBXNzc5w8eRJnzpzBsWPHWM6YDG5ubnByciINXGcwlhiG5cuXE2dNZ2dnXLp0SaWICQgIgLu7O3x8fEgeBF7scFJDQwO8vb3h7OyMsLAwbNu2TeV5qqbEv+p6r62tDb/88gs8PDywdu1a0siPHj0axcXF8PLy4rxjQUEBxxF1y5YtLN8/dSJm/fr15Pgvv/yCiooKVFVVITk5mcwQUidimKnp1tbWZPhFWcQozirrSbq8KLpSL3z11Vfku19LS0xMTAx+//13BAQEwMnJCd999x2Cg4NRVlaG6dOnqzVlLlq0CBKJpEsiRi6XY+/evWrjSrxMuiJiFixYAGdnZ6SmppJMVlJSgry8PACaObipsjYtX76c4zvBjJP/+OOPaGpqgkAgwOzZswG0C4i//voLu3fvZg1jPH/+nDQuixcvJhkwIyODDAt5eXnh+fPn+PDDD0kBunnzJonzIJPJMHDgQE78g84sMbm5ufjll19ITykkJAT+/v5qI5F2Jz0rKyvx448/kn1NRUxtbS0sLS1hbm7O+h6G8vJy8t6KM3QWLVpE/s7Pz8eBAwdIGvz222+or69XK2JEIhF0dXVx7Ngx1NTUcCwxL5uuihhFPyoGdZaYxsZGznBmfHw8Z0aFqinW3S0PjY2NyMjIwLlz55CQkACRSISioiL8/PPPGDlyJOkNK8dbkUgkqKurg5OTE1pbWyEQCGBvb4+CggIEBgaisrKSk6/b2trg6OgIABzfDHUoipjIyEiWsyzjk6BuijVz/YQJE2Bubk6cohkxuWHDBiKagfbgoevWrdPIOby2thaXLl0iZT0lJaXLIuZV1nuhoaEkjaytreHt7Y2rV6+S/LZ27VpOpGwfHx+WU355eTnHL1OViJHJZPj111/J/pw5c3Dr1i1kZWUhNDSUfIM6EbNo0SIUFRUhOzsbI0aMQFtbG7S1tcnv5urqir17976QdHlRaFovpKenY/PmzfD09ER1dfXrJWIKCgrwySefwNTUFF5eXsjNzYVMJiOVflhYGGxtbaGjowM/Pz+WCT0rKwuBgYEAgKamJvzwww+c3t3u3btVDiedOnWKYx14Fbi4uBDB0BF+fn4YPXq0yp4z0+C7u7uzCjOfz+eMgTLm4rKyMmRnZ6OkpARDhw5lOV+FhoaSCJ6McAFAKhBGcGzcuJGM2wLtvWOm0CxZsgT379/nvOv06dMhFotx+PBhNDc3o6ioiAzPMDAVcFtbGx49eoSGhgZMmjRJbfjvyMhI6OjocGY9hYeH49NPP4WHhwfnmu6kZ2VlJWvYg8/nY/369WRGFLPdvn2bFbQOAC5duoTff/+d7Ovp6eHWrVus63JzczF48GAivBR7R5mZmax9ZmrinTt3VIoTW1tbFBQUoKWlBdbW1vjtt9+wYsUKjrkeeDnLEGiarxlUOfaWlZWpFDE+Pj6kAWOIjo6Gs7MzGhsbSdooC6PulAehUIhvvvmGNNpisRhisRg+Pj6wsLDA48ePcezYMSxbtgwikQi2trasZ1ZVVcHQ0BAHDhxAdXU1EhMTSQdMKBRCX18fCxcu5Ihte3t7eHp6shqdjli5ciUKCgqQnp7OCewokUggl8tx8uRJldGyjxw5QqKoHj16FNevX8fkyZPJ/z08PFhltLCwEFpaWrC0tNTo3RS5cuUKLCwsSCgExW3cuHGc819lvScSibBkyRJSzpqbm5Gbm4sZM2aQGUWFhYWYOHEiK47Vli1bWGX9999/5zjpL1u2DPfu3WMdY4bUGJSjyJeVlaG8vJwV2kExXRSDv5mZmSEvL49lTUtJSWHNputuurxINK0Xli9fjpqaGrS1tWHdunVYsGCBRpF+/xEiBlDt4DVr1iwEBQURs25jYyNWr14NY2NjCIVCiMViuLm5kfOFQiGZmqfItWvXOKbhhoaGv21tIEdHR058DFVIJBK1oezT0tKwdOlSvPXWW6wZG3FxcSzz565du1gNZHR0NN577z0sW7aMdb+FCxciJycHQLsZdc2aNdiwYQM2bNiA9evX47PPPsOTJ0/w7Nkz1iyOtLQ0UtkcPHgQo0ePhqGhIdmmTJmCkSNHspYr6GxmRnJyMgYPHowvv/yS879nz57hyJEjCA0NVRtj4+bNmxg0aBDneHfSs7y8HJMmTSL7ly9fRlhYmMoKOTc3l9UwJSYmYsmSJWQ/JycH+/btg729Pdn27t2rceRUuVwOLy8vjBs3jvTaGWJiYjhOk9nZ2bC1tcWUKVPw448/YvDgwRg0aBAsLS07XYOpO2iarxlGjRpF/pbJZPDx8cHw4cM5Aq2qqoplFWC4dOkSQkND0dbWhvDwcHzxxRecYZrulgdF4Xf37l34+PhwpuCGhoZi0KBBLDEWGxuLw4cPo6qqCvX19XB3d8cPP/zAqsQlEgnMzc1VhnzoytIoy5Ytw/nz51XWY8+ePcO+ffvQt29flrNlUVERXFxcONFRAbAayKKiIjKll+Hhw4fdWm8pISEBq1evVllemNlGiryqek8ikWDfvn2sWTH3799XaXmJjIzEhx9+iKysLOTl5bEsUn/88QfxB1TE3Nyclc5tbW3w8fFRm05Au7Xp999/x9ixY4lIkkql8PPz4wznM22mopWrsLCQtVRLd/P/i0STeiE4OJhTf50/fx5GRkb47rvv8Pnnn+Pzzz/HoEGD4OTkxBK4/xgRo4onT550OH5fUVHRaQjmfyL3799X2TvuDozwYNAkPTprwJqamljTqysqKjT2o2hubmbF7ejuYoclJSWcShSAxuvVdLQeUUcopyegPjquJqiK5dJT4uPjWSHKW1paNPpemUyG+vr6l+YT09V8rTjTiOHmzZucRe40jadSUFDAsdy9iPLQEU1NTaRxaW5uVinQU1NTux3UriMKCgo6va+iD6FUKuVYBl4FIpGoxzOylHkR9d7jx49Z+VVdeAIGRjQolumKigq1wrOgoIBV1pTD/qtDKpWyrmtra+t2O/ey878mdFYviMXiTgNjAu35SFXMqX+0iKFQKBQKhUJRBxUxFAqFQqFQXkuoiKFQKBQKhfJaQkUMhUKhUCiU1xIqYigUCoVCobyWUBFDoVAoFArltYSKGAqFQqFQKK8lGosY5UBPnSGVSlXOie8oUmhra6vKhQI7o6Wl5YXFXekqXU2XsrIyTsCoZ8+evbTYHS+D9PR0jWN3KMJEwHzZlJSUaByXQyQSdTuWDdAenEwikaC6ulrjQGAlJSWc6J7quHv3Liv2iaYBx3qaz4RCYY8WR21paVEZvflVoCrWz+v8HEUaGxs5Uao1vU4xVotcLn/p8UG6QmFhISuGTUfxTf6uOrQrdX1NTQ0rGF93efDgAcRiMR4/ftyjWEMtLS3drudEIhEnvRsaGrqc3iKRSOM4OV1BYxETHx+PxYsXk8arubkZMTExas8vKyvD3LlzWSvM8vl8LF++HE5OTiqvqaqqwo8//kgKG7OYH7NdvHgR8+bN4yRocXExpk6dCrlcjufPn7PCLDPbihUrWJErr127Bjc3N0gkEhQWFqpcuTkxMRGGhoYdRpjtaro4OTmRRb4Ydu3ahRUrVqi9RlOSk5NJxkpKSnopAbakUim++OILjcJBK/Pxxx+zov32lICAAJXhrDdt2oSAgACN7tHW1oa5c+eS5d+7SmRkJGbOnInm5mbMnTsXcXFxnV7D5/NZayoB7eIkNzeXc+6OHTtw+vRpsr9gwQKNVhLuaT5bsWIFLCwsNDpXJpMhLS0NFhYWZIXkhIQEfPvtt91e3qCsrAzOzs5oamrCnTt3cPz4cY3ys1wux6BBg5CZman2nPr6ehQVFZHN3t4e+fn58PX1ZUWTTU9PJ8ttKPPs2TO8/fbbKCgoANC+IrCHhwcaGhpUBkx7UfUN0B7iXnmZAVW0tLTAysoKNTU1qK6uhqmpKWnIUlJS8OWXX/Y4GOPRo0dJQL38/HzWgqzM5unpySn3ra2t2LlzJwlemZiYCGNjY8jlctTU1ODbb79VGQUX6Hre/jvq+sLCQlakbyaAneJ24sQJ0pY1NzerXCrFwMAAx48fR3Z2NubPn69ReWptbcXDhw9x8eJFHD16FKampvj44485a77dvXuXkw5JSUmYP38+K4L+vXv3YGBgwLo2JSUF+vr6Hb6HRCJBamoq2U9MTMSBAwcgkUiwc+dOhIWF4dq1a4iKiiJ5xdbWFrq6ul2qNzoVMQkJCaRh3Lp1K1mwKzk5Ge+8845ay0llZSXmzJnDOb5t2zakpKSovKauro61SN7o0aNZ/6+trVW5+mtZWRmpcOvr66Gjo8M5x9DQkCN+1q5diz/++AO1tbUoLy9HY2Mj9PT0UFpayoo6q0pxdjddrK2tOZEbx40bh8ePH6s8X1P++OMPItKOHDnSZQuRppw6dQrLly/HqVOnVC4V0RH9+/dHbGzsC3uX06dPsxYI8/X1hUQiwY4dO7rUC7p16xZZN0oR5bVLVCEQCMh6Mvfu3cPRo0dZDWhISAhiY2NZQv7QoUMwNzdnHfvjjz9Y4cEfPnyIDRs2YPv27SQ8fVtbG0aNGsUp4KosMz3JZwUFBRg4cCDi4uKQnJxMtt27d7Ma6Pr6epw/fx6urq74/vvvkZWVRcrEmjVrOGHSu2It9fLyAo/HQ3BwMDZu3Agej6fRb5qamgpTU1PO8YcPH5LfpaamBtnZ2XBwcMCRI0fw4MEDCIVCTJs2DUKhEE+ePIFQKMTt27exYMEClc/x8PBgNaQBAQFkkdmTJ09i8uTJnAaxq/WNXC4nVmbF7f79+0hISGAdy83Nxfbt2zlCz9vbm6yJtHjxYrIuk5WVFWv19O6Sk5ODkSNHAmjvaauy0Hp4eKgUg+Xl5Xj//feJdYlZX8rGxgZXrlxR+8zu5O1XXdc/ffqUtQZYYmIiR9wJBALWciyHDx/G2rVrAbQvI8Ks28Z85y+//IJnz57h6dOnrDqmpaUFCQkJiIqKQnBwMI4ePYqIiAikpqbi4cOHqKysVLl+FrN6dl1dHUaMGIErV66w0uHq1auQyWQoLCyElZUV69r09HSyQOa5c+dYddy0adMQExODc+fOwdHRkViSL1++jPXr18PDwwOpqakkIntDQwOampqQlZUFOzs7lZHaO6JTEVNZWUlW+BSJRJg1axYAYN++fR2u5ltTU4Nx48ZxfjQTExOOiBGLxWhsbIRQKMT06dPx8OFDxMfHcxYHq62tZS1O2NzcDKFQiJycHJiZmSE6OhoJCQkYO3YsMjMzWdu0adM4lQqToRVRXFVVVc+4u+mSkJBAKk2BQAADAwMkJSUhJycHU6dOJRnAxcWF08vojAcPHsDIyAgtLS2kQVYO3f4iKC4uxrBhw1BXV4eWlhasWrVK4yEHiUSC//znP92y4KhDeZXTn3/+GXK5HHZ2dqTBU6yonz9/joCAAFZ+vHDhAg4fPszpQfr7++OXX34hFZQqMcLn8+Hh4YFp06aR/bCwMBw9epRjuj19+jS5t7OzMxYtWsTqfSiHgz9//jx27tyJ3bt3Iy0tDTKZDGFhYVi3bh02b96MqKgobNmyBXw+H59//jkZGuhpPpPL5ZgzZw4yMjJYjWReXh4GDRqkdihDscMiFosxduxYxMfHk+d5e3tjyJAhGvf829raYGhoiPLyctTV1UFPT4+TpiKRCH5+fqznTJ06FSdPnuT8TqdOncLJkydZ1/v6+iI4OJjsz5gxA0B7IxkYGIjS0lK1Cx5OnTqVtdRDdHQ0Dh48SPa3b9/OsSR0tb6RyWQIDQ1FUlISS0x2tCmm75MnT9DQ0AATExMA7evoZGRk4Pbt26y1qDQR6x2hvFIzQ0lJCdra2jgrTl+9epXk/X379rHKxB9//IGDBw9CIBDgzJkzrFWXe5K3X2VdD/z/hUwLCgo07qA2NjZi7NixePz4Mf766y/s378fJiYmJK0WLlxILFtHjhxhDQ/m5+fD3t4eUqkUVVVVMDMzI+XF398fBgYGapdHcHd3x/Hjx1FWVsYSZBMnTkRxcTGKioo4IiYzMxNmZmYc0ZyRkYF58+apfM7ly5dZYpZZuFkmk8HW1lbjhU+V0Wg4adOmTRyFOnLkSLWLdAHtDYY6S4zygmXPnj1DYmIigoODMW7cOKSkpCAjIwM///wzqyIKDw9nWWpqa2tx/fp1XLlyBfPnz0dubi6ys7Oho6OD2tpa1NbWwt/fH1VVVThy5Ainx8pkgtbWVgQEBCA0NBQ///wzyTRDhgxhKeWepEt8fDxnSfS2tjasX78eFRUV5Pjx48dx5MgRtc9UhYeHB3g8HhwcHHD48GHweDy4u7t36R6dIRKJMHPmTOTk5ODmzZt4/vw5Hjx4AD09PY3WvXjy5AlGjRoFZ2fnF/ZOjIhhen9MfrOzs8PJkycREhKCwYMHd7g6KyOANUGxEW1qakJLSwtKS0tZ+VydqGMqPgCsXicAGBsbk2EJhq1bt8LLywvm5uawtrbG/v37YWVlhbq6OgwZMgQymQzff/89AGDo0KHkup7mMwcHB1hZWbEWuBOJRBg/fjyioqJY5zY0NJChl8mTJyMsLIysfpyYmAipVEqG6Q4cOIDr16+rTBtV3L59m6yiHBcXp9FirdnZ2Vi1ahWA9l7+3LlzO/StUBYxM2fOBACsX78e169fVytikpKSMGLECFajkJqaiq1bt5J9gUDAGVrsSX1TVlbGWTm9tLQUly5dwtatWzmNydOnT2FpackRc4ygY/52dHTEwIEDe+SvxvTcz5w5g4SEBPI9f/75J1JSUjgiRigUorW1lQyrWFhYYN68eTA3N0dISAjKyspYwy7Mt/Ukb7+quv758+eIjIzEqVOnMGnSJISEhODixYtq76u8EKdcLoerqytqa2vxzTffEGsHwBZdyjQ1NWHZsmUIDQ0F0C7Eg4KC0NTUhJ07d5LvV+7cXrlyhbVa+caNG0nZZ+osZRGTmpqKzMxMLFy4EGZmZizfKjMzM7XrT5WVlWHhwoXYv38/bGxssHr1atJOl5aWYu/evaitrcWtW7dUWo7UoZGIuXTpEsv34P79+52OhwmFQrWWGMUKUhE/Pz+MGTOGNBbKP1ptba3KoSKhUAgLCwvI5XKkpaWxxu/GjRtHzNhMJiwuLsbZs2dhZGTEymApKSksBa9KhCnSlXRJSEjgFMAbN26wCjfQPsasaF3QhNraWuzduxclJSWoqqrC3r17u+2019raCj6fzzpWX18PMzMz0gg5OzuT5d7T0tLwxRdfwNHRUeXiXAwpKSlYsGABa8XV7vL8+XP4+flh06ZNsLa2xvr161FaWoopU6YgPDwchoaGxBytKHpVkZyczHkndQ6pI0eOJJW/lZUVnJyc0NzcTCyGQqEQX3zxhUrRpKOjo3Y4acqUKZxe++bNmwEA9vb2SEtLQ3JyMtLT0xEREYGtW7ciJycHxsbGaGpqwvDhw8l1PclnERERiIiIgFwux6xZsxAbG4vW1lbo6enB19eX801tbW3Iy8tDXl4e/vOf/6C0tBRFRUU4fPgwgPYK+YcffoBEIkF2drbKNFUHU7lr6iAsFovx66+/ksa4pqZGbfkViUS4cOECtmzZAjs7O9jY2CAvL4/klZUrVyIrKwulpaVYuXIl5/oFCxZg6NChePr0KcLDwyEQCBAUFITJkycjNjYW0dHRiI2NxbVr1wC8mPomIyMDOTk5ePDggcpN2Uq1atUqlf5iDBUVFZDL5dixY0e3JlPU1dXB398fR44cwfLlywGA1cjL5XJSDpRFjCI7duyAq6srEhISYG9vj4qKCtjY2GDixInw9PRkibPu5O2/o64H2q0bzP9TU1NJWQ8ICMCGDRvA5/OJNVUx/evr67F//34cOnQIZ8+ehZmZGbKyspCVlYXhw4cjKysLq1evRkhICLmmoaEBGzduhKurKyIiIsDn8+Hm5oawsDB4eXkhJiYGfD4fBw8ehLa2NhEycXFx+Oyzz7BlyxasWrUKPj4+OHfuHAYOHIiKigro6ekB4IqYFStWIDMzExs2bEBERASxRt29exe9e/fG2bNnVfqv5eXlkXYDaK8/BAIB0tLScPHiRSxYsABpaWlYuHAhvLy8NF40VGPH3l9++YUMx6xcuRIJCQkdnq9uRVBm5oivry8uXbrE+p+RkRGmTJmCefPmoaSkhFhiAgICiCVm2rRpnHsKhUJMmDCBKH9tbW2SacaMGUOuXbt2LaugWVpaoqCggJjM//jjD/j4+JB9PT09NDY2wtbWVu2MEE3TRSAQcArgzZs3kZeXh4iICHLcycnphTq/AqrN7uo2X19f7Nixg/Qyb968CTs7O1bDrNwAFhcXQ1dXFz///LNa06mLiwscHBzg6uqq1vzcVRhLTEREBEJDQzFlyhRSMTPDSZ2JGE9PT2zbto0ltP/1r3+xnMAZFIc3jx8/Tpz6fvjhBwDtvhCrV6/mXNfS0oK//vqL5Kv4+HjY2dmR/YCAANY48J07d8jMF0bEMJYaAwMDCIVCHDx4EH5+fnjy5Am0tbXJtd3NZzU1NazfpaCggDTsihWmKoKDg9GvXz94enqisLAQYrEYycnJEIvFmDlzJmdl6ReNVCrFzJkzWZae2tpaVsOkPBtMIpGotcQYGRmhvLwcpaWlWLZsGeu6wMBAJCQkcDpYMpmM5cipihdR3yjT0tKCY8eOcRoNBwcHPH36FL6+vpwyfvLkSbz77ruIj4/v8axOgUBARGtMTAw2b94MPp8PZ2dnGBkZAVAtYoqLi7FmzRrSBsTHx+PPP/8k/7937x6++uorlsN9T+rQV1nXA+15ady4cVi9ejXLOpuVlYUlS5aovc7JyQmnT59GeHg4MjMzsXLlSgiFQgiFQowdO5b8rWg52759e4dllGnz1q5dy7JotrS0EAuWoaEh8b0LCgpCc3MzKT+KIiYqKgoLFiwgIgb4/zPf9PX1sXDhQvD5fOzfv5/jl+ng4IADBw7Ay8sLp0+fxrZt2/D06VOkpaWhqqoKVlZWkMvlHKt0Z2gsYi5dugRra2s8fvyYjB2rIiIiAvHx8YiLi0NcXBwEAgE8PDywdOlSVkMREhLCcvyrq6vDokWLoKuri8jISJw4cYJUFN7e3gDafwxbW1ti1haLxQgNDcXs2bOJM19iYiIcHBzIfTtS2JaWlsjKykJoaKhKj3XFTd2QhKbpkpSUxJo1NWHCBJK55s6di5s3bwJoH2dVrFirqqowfPhw1vDDq+LJkycqG3NXV1eV1qKOptzp6+sjNTUVjx49YplIewIjYpghA+a3trW1xa1btwB0LmIWLlzIslrV1tYSR0VlJkyYQP4+fvw4yb86OjqorKzEpEmTkJeXx7kuKioKycnJJC+5urpi2bJlZF9HRwfXr18nDruKjb69vT2cnZ2RmpqK5ORk7N27F1KpFCNGjEBjYyMyMzMxf/58cn5385kiDQ0NuHbtGvT19Uk6Mqhy5jY0NMTkyZORmpqKBQsWQCqVYsiQIXj8+DFmzZqFixcvqsxHLwo/Pz/88MMPqKurg5+fH+m0jB07ljTcZmZmpJJmUOcTM378eGRkZKCoqIg11CgUConztSrT/sSJEzvsPfakvgkNDSU9asUtPj4eMTExHQ6ZMojFYty6dQt79uxhDb/0BEURk5CQQP5OTk4mgkNRxDQ3NyMsLAznz59n1RcWFhYq85qi+OxJ3n6VdX1tbS309fVhaGgIFxcXxMbGIioqCgcOHCAiJjExkTW009TUhHv37uG7774jZUVRKADqh5MOHTqEuro6BAQE4MyZM4iNjSVpFBcXhxEjRqCysrJDR25DQ0NSDzIdUVXDSXV1dairq+O8m52dHeLi4mBnZ4fk5GSIRCJMnjwZ0dHRANqtLowly9jYmFyXkZGBdevWoaqqCgYGBoiPj8e6deu6JK67FOxu4cKFGDVqlEZe7RcvXsT48eMhlUqRlpYGGxsb8j+pVIr8/HzW+X/++Seys7NJoyOXy8mPdvDgQTQ3N8PFxQW2trbEByMzMxPGxsZ4+PAhmZ20f/9+hIWFobi4GBKJBLNnz4ZEIkFaWhpnfNzS0pI4tQmFQri4uCAsLIxsHh4eHTovdyVdLl++rHI8F2gXaYyjnZ2dHavXUlFRgcGDB2s0rfZVoU7EqKO8vJxlxdDX19fIj6YzlB17GRGzbds2hIeH4/bt2x32jpuamjgiIXANMQAAIABJREFUNzs7m/QglRk2bBipHNauXUt6fwcPHsSqVatYhRNoL/BnzpzhDKkqO/Zqa2vD29sburq6pNKuqKjA4cOH8eOPPyIqKgptbW2wsrKCVCqFr68vdu/eDaDdJKzot9HdfMZQW1sLCwsLjBs3DjExMaz3TkxMxOeff86qYK5cuQJvb2/icB8UFITo6GhSbpjjJiYmGsfG6Qpnz55FXFycyqHnzoYIlEXMtGnTIBKJMH36dJibmyMzM5PVw1fMs6oalBUrVnQYO6Yn9c39+/dV+ve0tLQgOTm5w+9kOHr0KKuDBwB79+7l1MW1tbUaO+wripikpCSWiGGepShicnNzyZCH4jZo0CCEh4ezjh07doxlyetJ3n6Vdf3BgweRmpoKQ0NDyOVyyOVyzJw5E6mpqUTEVFZWYvXq1cSCVlpaikWLFrFmSTIdFKb8fffddxAIBLCxsSEz4ZRpbm5mzXLcsWMH1qxZo7KDqehgPWnSJISGhiIiIgL79+8nVlSAO5zEvBsjYry9vYkliBExQPuMwA8++AASiQSPHz/G+fPnyfA5n8/HvHnzyEQIxhKjis5CDmgsYuRyOVavXo0hQ4bg8uXLnZ6/efNm4mSUlpYGIyMjkmDBwcE4dOgQSdiGhgZERESgpqaG1XNmKoq7d+/C1NRUrSMx4xMDAKampnj48CH09PRYFfCkSZM4vUEmY9+8eVOlmU9ZfPUkXVSZQpnGQNEUvHHjxk6H6v5uuipirKyskJiYSPZTUlIwdepUlQWruLgYNjY2GqUBI2Kam5vx4MED0mjZ2Njg8uXL2L59O/bs2aPWCnD06FGOw2h4eDh27Nih8nzF2DPV1dWk152bmwsej4eHDx8CYDd2qnrmqhx7S0tLyX5jYyPGjx+PK1eukNlJp0+fRk1NDSoqKjBp0iTSyHh7e7Pu1d181tDQAG9vbzg7OyMsLEyt75Ji4y2TyeDg4ACZTMZyXDYxMSENDyNiLl68iN9++411Lzc3Nzg5OXFmDWnKkydPiD9Bd0WMjY0NAgICcPfuXdy+fRthYWFwcXGBubk5GVJVJbhViZgLFy7A1taW7Le0tLCmw/ekvmFmqikL4nPnzmHw4MGsIRCpVIqAgADExcWxRIGlpSXs7OzIfmxsLHx9fVlDNlKpFO+99x7GjBnTYdoxMCLm0aNH8PHxwapVqyAQCODi4oJ9+/YBYIsYsVjMCRFw+fJlLFq0CFevXu3Qd6ondeirquvr6urg4eFBZicB7QKUubfis3ft2sUSlVFRUSTNAM0tMcr4+voiNTUV0dHR0NfX1yjmiqIlhmH8+PEAOhYxoaGhrDpUUcQA7fUp89sw9f3UqVOJIOfz+XByclIrYmQyGYYMGdLhu2skYurr67Fo0SKcPn0a1dXVmDRpEg4ePKjW818kEkFXVxfHjh1DTU1NpxmEqSyeP3+uUsQA7WOQig2hIoyIKSwsxPbt21FSUsIJ1KUqTsyqVatw4MABnD9/HkKhUGXG3rt3r9r37kq6qHJKU9XbWb58OcuXqLm5GevWrVPrDP130BURc+nSJdbUUwYrKyssW7ZMpZCprq7G119/TXp1qhCJRNi8eTN+/fVX+Pn5sawqkZGRrF6/qkby8ePHsLa25hw/cOAA/P39Ocfb2trw8OFDxMbGwtXVFYcPH0ZgYCBiY2Ohr68PMzMzEmxLUezIZDIEBQWxZm2ossQoO/YyBX/37t24fv06mpqayLRjxSGrXbt2wcfHh+x3J5/V1tbi0qVLpEFISUnRSMTcv3+flCkmflNkZCTLN4URMXK5HF9//TUrPR0dHQHghcxY64qIefDgAVavXg1jY2NWuIfW1lbo6uqirq6OJWIUrTHqnge0C4CJEycSa0xISAirAelJfbN7926EhYVxjguFwk6HTBlcXV1V3kMRuVyOvXv3atxgCgQCLFiwgAx5MuKhpKSE5NOOHHslEgl0dHRIiAF7e3u1MWK6W4cCr66uLy4uhkwmQ2lpKZlqvGnTJvKeiiKmtLQUgwcPJtfu3buXVVd2JGI6c3rV09PD6NGjNbaoMSKmsbERKSkpqK6uJsN76kTMokWLONZVZRGjDJ/Px5EjR2BiYoLS0lJs2bIFERER8PX1VSli5HJ5p0E3OxUx4eHhWLJkCWuGQEtLC5YtW4avv/4abm5uyM3NZY3v2draEicqa2tr/Pbbb1ixYoXKcS7FQv7s2TPW7CMmgFFwcDBqamqgra0NBwcHjif+48ePYWlpif3796O+vh7FxcUwNTVlTUWcPXs2JxCTkZERMcuXlZXByMiIdU10dDT5f0/Thc/nc8ZzmZgOZWVlyM7ORklJCYYOHcoaky0sLISWlpbaeBV/By4uLh3OfGC4fPmyyiiUQHuPbMaMGRg1ahQiIyM5zp8XL15kNXqKZGdn45NPPoGXlxdLBI0bN45TqUilUkyZMoVYSYD2njXjWwK0O9IWFRVBJpNBR0eHYyquqKjAu+++CyMjI/j5+aGkpARA+2+zbNkykgesrKwwb948LFq0qMN0iYyMxJ49e8j+7du31faWdu7cidTUVLS2tmLjxo24f/8+q/c5YcIEVoXd3XymyJUrV2BhYcGKXstsyrGbGJiAakwDXlVVBZlMRmY4AFx/Gnt7e3h6enY7PoQinYmYqqoqMsOqrq6OE2lVLBZjzZo1yMjIAACYm5uT9929ezenzvnpp59Uvkdubi6GDRuG1NRUEriMoSf1jb29Pfz9/cmUVGZ7+PBhh/4ZQLtlTy6Xw9HREeHh4R2eC7QHtFSedKEOPz8/jB49WmVnhCmL7u7uKkWMSCSCqakpazqtVCrFunXrYGpqiqtXr7LSvSd5+1XW9UC7mJk7dy5SUlJQUlICsViM1tZW3Lp1C0uXLiXnKfppbd26lRXNPj09nTWc9NNPPxHr28SJE1UOz1ZUVGDNmjWwtbXFxo0bMX/+fERHR3PqV7lcjuzsbISFheHAgQOYMGEC7O3tERQURKybzG+anZ2NdevWsa5PT0/nHAPanYzVWamqqqpga2sLuVyOlJQUCAQC0lmKi4vDjBkzYGlpCTc3N3h6ehILqHLQTGVUihipVIrY2FgcPnwYf/31l9qLz58/j88++ww6Ojrkw2NiYjgOWtnZ2bC1tcWUKVPw448/YvDgwRg0aBAsLS1ZJqyKigpMnDiR7H/99ddYt24dScympiZYWVlh1KhRpOcaGRmJTz75BBs2bCDPzc/PJ3FjmG3GjBmcKcDl5eVE0RYVFWHBggWsa4KCgljqvCfpEhcXxxqr3LVrF+nFyuVyREdH47333uPMhgA0Xy/nVeHo6Ijjx4+r/X9raytCQkI6jLoJtFcEpqam2LVrl8oYDR0FiVLlYLpjxw5Mnz4dc+bMIZuuri6MjY1JYLLbt28jIiKCJRrEYjHs7P4fe18eFdWVrZ/kda/O6nT3S7rf66Rf+iXRztRR2znOxuAQkyAoICoqKI5EEUURlGEpKsg8CDIoKIg4gspUQomIogQREVEBmed5LMYa7vf7g3VP31P3VlGASXfer7617lrUpeqO5+zznb332d9BvPHGG9Sgq+p8DMMgNjYW4eHhPON9+vRp/PnPfxZcFlxYWAg7Ozv84Q9/oGozqIOdnR2uXbuGc+fOEYOdn5+PjRs34r333sPSpUspV/po2hmL5ORkbNu2TZDEqMoXUq6uHR8fj48++oiqnSIEVQW4hovp06cjPj6eLCZITk5GWFgYFXbx8/MTrChbWloKFxcXquhbYGAgdHR0YGxsjL/97W+8Ap3jxo1TeS3Pnj3DhAkT8Lvf/Y6auA3X3nBhb28PFxcXZGZmUltiYiLmz5+v9tkUFRXBzMwMv/71r3l1SZTR1dWlUU0eFjKZTGWYPzMzE2ZmZvjNb35DVVuuqqpCaGgobGxsVK5UvH79OiZNmgRdXV1y/NG07Z/T1gODq/u4S7D7+voQHh6OP/7xjyrzWRQKBdU+MzIyNFoE0d/fj9u3b8PHxwfHjx+nbGlRUREsLCzw3nvv4d1334W+vj5p58bGxti3bx+ysrIEl0RXVlbi0KFD+N3vfsebjGZkZAhOrPfs2SNYF4etn8O+g7q6OgQEBPAmnQ8fPoSHhwch2/39/Wrr0QEqSExbW5vGyyKlUinpqH19fVQVS1VQKBTo7OzkDQC9vb1UY1dOOGMh5InhGou2tjZeBcahHsTAwIDgLJ6LkT4X9vNQ+HcSZFMHrjdACG1tbSPWzPmpoS659P79+xoLlKlz08pkMrVia8p1eNShpqZG0HXMMAxlFFi8inbW09OjcY0GFkL9q6qqatjHGSm4Gi3DQXNzM2/SpQmGIuhSqRSxsbEqSZom9oaLrKwsQc9ZX1+fRiu/2AUW/wpwk50bGhqQlZWlUTsF6LDJq7KhP7WtBwa9fUITGVV6UEJobGwccgEEW75hqO8xDIPS0tJhS8UAg8RC2S5WV1cL3l9BQYFGwpCarKbTFMNanaSFFlpooYUWWmjx7wItidFCCy200EILLX6R0JIYLbTQQgsttNDiFwktidFCCy200EILLX6R0JIYLbTQQgsttNDiFwktidFCCy200EILLX6R0JIYLbTQQgsttNDiFwmNSUx2dvYrPbGqmghyuVywVsfPUXdEJpNRGjaaQKFQ8GrSqMNIi3txC3GNBmwdn6FUbNmqtCNBR0cHVTdBXc0ULrjFomprazWq2TJcsAXjuBhKYOyXhJcvX2r83ba2Nqou00hRUFAAqVSK0tLSV/7ONFWz1aQ+1WigaRt5VcX7uOBWgh0KI3mfDQ0NGtdEeRUoKysjMgOA+vovtbW1vEKfTU1NGtUiYZGXlzfsekXK9VR6e3tfaRtjGIZXB628vHzYRU1/6nbZ2trKa39DjcXV1dWC7Wmo+lnKUCemyoXGJEYkEuHrr79WW6RGLpcjOTkZKSkppFImVzOG/TslJQWzZs2Ct7c37xi1tbXQ19fnqZxu3LiRKsnMIi0tDe7u7rzvK28WFhaCpa+5BKS5uRm//e1vhzUQAIPqtdevX9fou3p6eggPDwcwWM5cqEiRUCOZPHkyKYnu5+dHSccnJyfz1GlVwdLSEj4+Pti7d6/aAmEzZ84khaM6Oztx69YtlJeXky0+Pp5cjzJSUlJgaWlJPi9atIiQJoVCIWgMamtr8de//pVUVT5y5Ag8PT01uqfhQF9fH1FRUdS+adOmoaSkhPfee3p6qHtWtz179gwODg4jLu52//59+Pr6QiaToaysjHq/7JaSkgIDAwO1hispKQnr168nlT97e3t5JfZZlJWVUSrfcrkcMpmM2k6ePEkMa29vr6CMhK6uLk6cOIEnT55gxYoVGk84lMX+0tPTeSTowYMHVKl5sViMmzdv8oQQ58+fT2k29fX14dKlS7h06RKlVszdjh8/TvSuhJCTk4OtW7ciNDQUrq6usLW1RWFhIUW2lTHS/q0O3377La+vKRQK2NjYoKSkhOzr7+/Hf/7nf1IEYSh0d3dj9uzZPH0xoWv09vZGZWUlgMFCpELvITg4GGfPnqV+19/fD3t7e/z4448ABu2DsbExGIZBW1sbvvjiC5VF4Nzc3GBra0vtc3BwUPvelLFu3TpKzwwYLNgpdI+hoaEYGBjAixcvYGVlRfpzZGQkFixYMCxCqQ5ZWVn46KOPqMHe39+fqkisCj9nu9y2bRuOHj1K7VuwYAGam5sBCE9QbW1tBcfqa9euqdRkUwbDMHjvvfc0cp4MK5xkZmZGDU5CaGpqogwRV5xsKFVZYLBKodD39u/fzyv9DQzKvytLqF+8eJHXkVUJU50+fRqbN28GwzCoqqoaUr21tbWVVyI/OTkZDg4O1L6amhoe0+7q6sLHH39MXvzDhw8xfvx4KBQKREVFITExEe7u7jAxMaF+d/XqVXh5eZHPixcvhkQiIdvDhw+xbds2tdfNVlSdPXs22tracO/ePdja2qK9vV3QO8FVJa6vr8e3336LgoICstnY2BD9JFbkkFXN9fT0xIYNGwiBnDZtGvnb398fNjY2vMHe1dWVIi1ubm4qReNGiv7+fkyZMgVyuRwKhYI8E3Ygt7W1pYxdb28v7ty5Q913QUEB5s2bhzNnzvD2FxQUaOw5EMKOHTsQEBCA9vZ21NXVQSKRYOnSpaiurqbet9AsNDk5mey3sbEhWkJpaWl46623BA1cTU0NUdoFQE0+uBu3vXt4eBBNoPj4eDQ2NmLWrFmkzy9btgxNTU2oqakhBqi2thbXrl1DUlISLly4AGdnZ8TGxmLZsmW4ePEiOc+YMWPIIMkiJyeHJwwXGBjIM/ZCKrwNDQ0qCUN0dDQWL16MiIgIyhA/fvwYERERcHZ2xtixY5GQkIDS0lL4+vrC398f/f39mDlzpmD12+H2b4VCgfT0dKSlpZHN2tqa59nV0dERlEvIzc2l7FV8fDzPPpeUlKhUhm5ubsbixYthbW1NvW9ra2vo6enxCGVeXh4mT54MYJDgC11TUFAQTp48ydtfV1eHd955BxKJhFJxt7a2Vlv92MrKCn19fdS1sJp6QoiLi8ONGzeoCez06dNx/vx5ap+pqamgfUlKSsKKFSsAAOvXrye6TqtXr+bJ1owGBw8exLlz59Dc3Ew2T09PnD9/Hs3NzaioqMC+ffsIafo52yWL1NRUIsWQlJRE7p8dG2JiYjBv3jwesVuwYAGxg6Wlpejt7QXDMFi6dCliYmKIGrnyuM1FRkYGVq9ezdtfVFTEIzYqSYxMJqMMp0QiQV5eHm7evEntc3Z25pUfjomJwY0bNyAWizF16lTSOb788kuIxWL4+fmp9By0tbWR73G3VatWCZKYtLQ03sPYvXs3T+9CFYlRKBT46KOP8OLFCzx69AiGhoaqHgmAQULh6ekpaOy521dffQUdHR3KVRoWFkaM7/Hjx1FaWsqbtcTHx1OCeB0dHThw4AAUCgV5zqwyMIv8/HyeSrhIJCKdOSgoCBMnTkRZWRnmzZsHsViMoKAgxMTEwNbWlpJ/7+7uhkQiwZIlS9DZ2YnTp0+jqakJxsbGAP6pyOrh4aFSrfTevXvUPQxFXmUyGSZOnEh5GDw8PIiR6enpEfTaDReXLl2CpaUlRCIRjh8/jvnz50OhUBAF5o6ODnh7e6sNiUilUqxdu3bU1yIElrxwwRU2VBe2bGxsxLlz5wAMPi/W0Dg7O6s0FrW1tTAwMEBJSYlanSouJBIJpk+fjtLSUty9exdHjhzBqlWrSLtfu3YtmZF7eXlRZLWgoIDSglm+fDl1v0LKybm5uTwSExoaStSYq6urUVFRIUhilMGWh/f09ERiYuKQ98q9ntDQUNIGExISBLXDRtK/q6ur0dPTg76+PvT09OCDDz7ghbTZSYtQyXiuy33VqlW4dOkSxGIxUUkPCAiAj48Pb9KQlpYGCwsLHD58mDpfQkICpkyZolLyRZXWUVVVFQYGBniK1enp6aRtODs7UyruAQEBOHbsGMRiMc6dO0epUrPeZS8vL4jFYujq6iI1NRV5eXlYsGABISOenp48Tw0AinCw/ZuFUDtpbGxEZWUlGIaBr68vgEEC2NHRgZSUFBKm6+npQVJSkuAz0BRSqZTYnlu3biE7Oxu5ubk4ePAg/P39kZubS7aOjg7e73+OdpmcnAxnZ2c0NjaiqqoKmZmZ+Pzzz9He3o4FCxYgMDAQ4eHhkEqlVKjqyZMn2LZtG3k/33//PdLT03HmzBnyXHt6ejBv3jzyHnp6enD27FkkJSWR3y1YsAChoaG8iMqpU6d4XkOVJEYikeDq1auUO9vS0pJ4RLibcsN2cnIihEPIE3PlyhWVKsgtLS0qPTFCwmRCJMba2pow0tTUVLx8+ZJHYvr7+4kSbGZmJtrb23Hu3DlYWFhQKrGRkZGkIcnlckybNm1IDZLm5mYcPHiQNxhOmTKFqBfr6uoK5qVwGxPDMIiKioJMJkNcXBwWLlwIuVyOhQsXUi82ODiYR2K4DevcuXPw9/eHu7s7kTXfv3+/4KCYnJyMtLQ0zJo1i7zfqqoqQmImTJgAAIIkJisrCwkJCaivr0dFRQUxSGw+D+uCVEZ4eDjmz59PuVa5JMbMzAx79uwZlQgmwzD49ttvceTIEdTX1xPPDztTE4lEEIvFOHz4MKysrKjf+vv7k2ft5+eHoKAgXueyt7fXWA9GFdiBpr+/H5GRkbh8+TJmz55NBoFPPvlEUCiTxZ49e3hemsmTJ/N0jVgxtlOnTmHu3Lm4dOmSoGgbC2XRQIZh4OPjg/b2dnz22WcUMREiIiyESAw3PK2OxCQkJBBSvmfPHhw8eBAikQhnzpxBTEyMRiQmJCQEK1asUBkSYBiG6vszZswgf4eEhMDJyYl8vn79Oi+0Ntz+DQwqqLNELisrC2vWrEF0dDQSEhJI25oyZQouX76MjRs3Us9LKpUS21RQUIBjx46pfZbAoEckPDychJJLSkqwcOFCMAwDLy8vrFu3Tm24kvUEnjt3jkoVCAwMxL1793gkpr6+Hv39/SQ8aW5ujuXLl8PExASXLl1CbW0tFb5kbWZSUhL1nPT09DAwMABLS0vquZ44cYLyUrPYv38/vL29IRKJcPToUfIsr127hnfffZdHDlxdXSnBUO7G9VDu2bMHX3755ahyn86cOYO5c+eiu7sbz58/R2RkJEQiEXbu3InDhw9DJBLB1taWeND+Fe3Sy8sLu3fvRmRkJNLS0lBUVIT9+/ejoqKCJ5J7+fJl8reDgwNaWlrQ19eHlpYWWFhYICsrC4WFhTh48CDS09NhZGQk6JBg8eTJE2zZsgXAYHvV19dXa1uHFU7y8fHBqVOnhvze4cOHhyQxqo5TX1+v0hOTmprK+z5LYhISEoiCra6uLoKCgiAWixEdHQ2xWMwjMc3NzYiPj6cUYVetWoVjx45R+8aPH09i7RKJBFFRUWAYBiUlJVi7dq2gu7qjo4NHYMRiMdavX0/cqHp6eoKDunJjYrFo0SIyGCmTvPz8fOzZs4fa9/DhQzx69AgAsHHjRpSWlmLFihU8EvP06VNBNyn3HBKJhJAY1jh6eHgIvg9dXV0UFBTg6dOnWLJkCTXQz5kzBykpKdT3pVIpdHV1MX78eOpZsiTG09OTRypGgsjISFhZWaGgoADfffcd9PT00NXVhYGBASxbtkwlqQYG3dcs2OfS3t5OuV///ve/jziUVFFRgejoaBgZGVFkgg35KZ9bFW7dukXdx4sXLyglXWX4+/uT/2dkZJD3FBkZiV27dkEkEuH69evYt28fFY7q7OzEkSNH4OrqiujoaKxZs4bMHCdOnIjc3Fxs27YNly5dos6nTGL09fVx8+ZN4gnQ1BNz5swZ4olh+5kmJCY8PFylgjCLxMRE3L9/H5mZmZgwYQKxA66urjA3N0dmZibu3LmDzMxMZGVlEeI5mv7t4+ODjo4O2NvbIy4ujkdElyxZQoVuKioqEBUVhaNHj8LMzAwA4OLiQpEzdWRSGdHR0Vi/fr1K71RHRwciIiLg5eWFjRs3AgB1jQzDEHKlTGK4OHDgAHx8fJCcnAwnJyc0NDTA2toac+bMQXBwMGUzk5OTeSTmxx9/5B3b29tbcCw5cOAAxGIxbz/rZeaivLwc48ePR09PD27duoWsrCzKG5Kbm4ujR48SwjGa5PXu7m6sWbMGixYtAjCY8+Xh4QFgMEzKvgNTU1OKaP3c7bKpqQmBgYHEJnh4eCA2NhYAMG/ePISEhODChQs4e/Ysxo0bh46ODpSXlyMmJgZLly6Fn58frK2tsX37drJIpLCwEOvXr8fTp09VPh+pVEpdY1tb25B27ychMY6Ojjh58iREIhFsbGxw+vRpiEQixMXFQSQSwcXFReXMr7OzE1lZWbz9VVVVYBgGZ86cwa1bt8h+IU/Md999h6KiImqfqnASF+PHj+cZwk8++UQwB6GqqgorV66k9qWnp2P+/Pm85GeGYeDi4kLFgodj5C5fvgx9fX3ymfXEXLlyhXhi2DwFLgICAvDy5UssW7YM165dw927d7F06VJCCk+dOoXg4GAkJCQAGHz2bGx+xowZSEtLw7Zt2/DkyRMYGxuju7sbM2bMgFQqhYeHB0VIoqKi0NDQgPr6emRlZaG4uJhnLIyMjHgq0kFBQcjPz+cZXQ8PD+jp6WH37t2jXvFSVlaGpKQkPHz4EEZGRkhMTCS5A3fv3sXBgwfh7u6usnPNmjWL/K2KxAxn0FCFrVu3oqSkBH19fejr60NAQADCwsLI56VLl0IikcDOzk6lV2rZsmVkJr1582YkJyerPN/ixYvx5ZdfYtu2bZQqd25uLkxNTVX+zs3NDeHh4bh69Sqys7OxefNm1NfXo76+HtOnTyd/K7dvZRKzaNEi9Pf3Y9GiRSgtLR2SxISHhyM8PBzu7u7w9fXFmTNnsGHDBgCakZgzZ84MSWK44F6PWCzG7t27AQy65Lm5J6Pt3xKJBJs3b8bcuXNJGH/Pnj2k3SuTGO41eXh4oKOjg2dzhgpDNjQ04OTJk9i3bx/c3d2xcOFCtQrv3POx97F3716IRCK4u7vDyMgIgDCJqaiogIWFBbHbSUlJCAwMJP9//vw5Pv74YypMw+ZNsNDT08OjR49QWFiImJgYst/NzY0XGgEAe3t7uLq68jym165d49klLmxtbZGYmEjsGNuWDQwMyKRwNPD29kZ9fT2xIw8fPiTP1Nvbm9hUU1NTlZOin6Nd1tXVQU9Pj3w+ceIE8bhUV1fjxo0buHbtGuLj40lSdnNzM3p6emBqaorMzEz4+Phg69atyMzMxOXLl3H+/HksWbKEl/fGQi6XY/HixVSCfnt7O0VihNqoWhLT19dH5b+4ubkhICCAfGbdb8pgB32GYTB79myVuSzc1TExMTFISkpCYmIi8agEBQXBzMyM8shcunQJN27coI7DJTEDAwNYtGgR/Pz8KAM+FIkpKSnBggULcOcTkSBSAAAgAElEQVTOHWrQfP/99wW/X11dTbwTra2tsLOzg6mpKVJSUnjLy54+fYr29vYRNabm5mbs3r2bSsBkX2pERASAf+bNKCfoymQyLF68GKGhoaisrER1dbXacBLDMKisrIRMJsPChQuJi5fNiSktLcXnn38Oc3NzeHh4UDO3hoYGTJ8+HQCIgVI2Fnp6epQxrqqqIgSK2zHlcjmMjY153qWRgvs+g4ODYWhoSGY5+vr6qKqqQl1dHWbMmCEYauBe209NYnJzc3H58mXB1UncTdUqwVu3bsHKygqlpaVktieE9vZ2fP/99zAwMICnpycSEhJw/fp1HD16lJCYlJQU6nmw7u9x48bh2bNnAAZLL4w0nPTVV18BAM6ePYsjR44MSWL8/f2pAZLNIwB+GhIzceJEREdHIz4+Hvfv34exsTFaW1sxduxYalXQaPo3i7Nnz2L58uXk886dO3H69GkAQ5MY9u9r166Rwfrvf/87RCIRrl69io0bN5IwZF5eHmJjY3Hz5k10dXWhsbERU6dOxc6dOykPNJs7oep8ycnJ5O+0tDRyT1wS09vbiytXriA2NpaaCJqbmyMnJ4c6dmtrKzVApaamUqvSZs2aRY6hr69PCIWzszMuXLjAezb29vYae2K4cHBwIOkC8+bNI9dpYmIy6uXz1dXVpN2wdiQjIwObNm2CSCSCmZkZCXEvXLhQMAcK+HnaJZuwzz7/HTt2ID4+Ht3d3VixYgUJ7yh71vv6+igSs2nTJrLq8/Tp0yR/LiUlBWZmZtRYefbsWUyYMAEdHR04e/Ysab9suF8kEmHNmjW8dA61JCY3NxdisZg0bCsrK9jZ2VGNnTt7bWlpIScXiUTYtWsXNm7cSD6fPn0a5ubmxBtz/Phx3ozy5s2bmDlzJuRyOTIzM6lcD7lczks4UyYxwcHBOH/+PIDBRDc2U34oEmNiYoIbN24gPT0dxsbGkMvl6OzsxCeffCL4/erqasyfPx8XL15EZGSkRjUEhtuYZDIZgoKC0N7eLkhivLy80NPTAwcHB7i5ufHCQgqFArq6umS2Wl1djXXr1qG9vR1WVlZ48OABIiIiBFcHzJo1i+QgNTc3Izw8HHFxcTA1NYWTk5Ng0hm7EoKdKSkbi7lz51KEQsj93djYCCsrK6xZs+aVr05iGAYWFha4ffs2gMFZHHclhZ2dHb755hveexmpJ6a9vZ3ycAyFrVu3kvyh+vp6eHp6UkuCg4KC1Gb0s1i7di2mTJmi1m177NgxZGRkwMDAAAzDgGEYLF68GBkZGYTENDY2Ytu2beSdse2Hm9icnZ2NFStWEGM3btw4ssJFmTAUFBTAxMQEMTExSEtLI7N3hUIBiUQyJIk5efIkj8QcOHAAwKshMd3d3SQfYdWqVfjiiy/IxGBgYAATJ06EkZERLl68KPj7kZIYtp+uWLGCzEJv3LhBSIImJEaZfHPbrCr8+OOP+P7775GdnU3lXNy8eRMff/wxzyPOPV9qaipFYtiFGlwSk5+fj5iYGJ435L333sPVq1epfX5+fli8eDEZ1G7fvi2YEwMMDoZsaOPgwYOCdsLBwQHu7u68tIT4+Hi1JMbe3p4MktOmTSN9yMTERGVO33D7OfBPO9LQ0EAm8z/88APxanDrdP0r2mVTUxPPExMfH4/+/n589913UCgUOHDgAFU+AxhM0l28eDH8/f3h4eEBU1NTtLS0ICcnh/ISXb16Fdu3byefo6OjkZiYyLMByp4YIfwk4SRg8CHo6OhQyxfXrl2rdjkdAOzdu5e4rTIzM2FkZEQa4IULF+Dq6kqxei6JycjIoJY6t7e3409/+hPa2trUkpj79+9TK350dHSQk5OD/Px8zJ8/X/A3XE+MEIRqNQy3MbW0tEAul0MikQiSmNLSUqxdu1blrDwsLAzp6elYv3490tPTUV1dDT09PWRnZ8PU1BQXL15EdnY2b3VZUVERfvjhB4SEhMDGxobs37ZtGy5fvkzugTtzam1tRWdnJ4yNjcn7YY1FXl4eVq1aha+//hoABAfX2bNn4/79+4iKikJ/fz+V2MtCOQTp6+sLNzc3Xra6EFpaWmBtbY3s7Gz09PTwZvXAYDx248aNvPyYmTNnkr81JTFyuRxvv/32kEv2uWBJzKNHjwRDOsqkXggMw2Dbtm345JNPCFlTRkdHB4KCgsjqJGAwf4Y9NvfcDg4O1ErC69evU6vZNPHEVFZWwsjIiOQedXZ24vLly6RuhfJvucnLyp4YR0dHMvAFBgaS9vkqSExvby9WrlxJwhrK9zJ79mzs3LmTfFYeuEZKYnx9fZGUlISMjAwsWLAAwGD7YXPEhiIxeXl5PK/DjBkzVN5na2srSVB/8OABjh8/Tv6XlZWFWbNmCbr82fO9fPkSYWFh2LJlC8RiMTw9PUmb4JIYqVTKyxm8ffs21q1bh/T0dJVLv9lzKZMYNrzCnQjt3r1bMGTq6Og4Ik/M/v37kZWVhaamJixatAhffPEFuru7sX79ejJYc9/7SPo5QOe3lZSUIC0tDcuWLRPMsfxXtMvm5mbMnDmTqrMWHx+PgYEBTJ48GW5uboJ1wjo6OmBmZoba2lqUlJRg5cqVuHfvHi5evAh7e3vU19ejp6cHc+fOJZ6tyspKknP3b0Vi7t27h5MnTyIyMhLBwcGwsrKCnp6e2kqLPT09WLJkCfz8/NDW1qaR0WZJTHJyMglNcCESiSCXy1WSmLKyMujo6FCeFNaNFxsbSxLZlDEUiVEurgSM3MipIjHAoPdA6FwVFRVkppSUlAQXF5chw0ksjh49StyoX3/9Nfr7+1FcXIzt27cjOzub3AP3vO3t7di6dSs1sHONRV1dHcnVEFoGqDxzFCIx3MF0YGCAGF93d3fB+2DR2dmJGzdukOWoWVlZZMbX2NhIEqYzMzMFDSt3QNCUxDAMg8OHDw8rzLRlyxYcPXoUsbGxqK+vFyQxbN0GVfe5bt06hIeHo7W1FXPnzsWxY8d4mf0VFRVQKBSorq4mIQx26TxAk5jq6mqMHTuW/Pbw4cPUKhh1JIa7rFckElH7TU1NeQab/e2ZM2fIPi6JCQgIoEon5OXlkXf/qhJ7ha6HtR9btmzBtGnTyCyYm5sBjKx/P3r0iHgDFQoF3n33Xd77UkViEhISoKurS1aecKFuUH3w4AH1bj799FMoFAqEhIRgzZo1gst62fszNDSEu7s7MjIyCHmoqqpCYWEhAPWJvTKZDDo6OmSC5+TkpHJSK5TYK+Tt2Lhxo2AOpaOj44hyYlhPqIODA8LCwkh/544PbFI5MLJ+DvCT9O3t7Ul9mqHwc7RLVZ4YqVTKK/HBzWWSyWRU+9HR0SFt7c6dO0hOToaZmZngSmPuvbH4WUhMaWkpZaCU8ejRIyxfvhz79u3D4cOH8cUXX2DlypWC9Qbs7OxIYqOVlRVWrlyJTZs2CSY4sQYwNTUVe/fupdyhQtvevXuphGBgsMGamZlRYZjm5maIxWKUlpbC0NCQN1tkUVNTo/Lh9vf3Y82aNbz93KJ4urq6lCeDTSDz9PTkDcodHR0UidHR0UFwcDD8/f3R3t6O1atXw8DAgCyF7e7uxqlTp8iMRSqVkjoIq1evRnV1NUm0Kyoqwo4dO8gzbmxspEIWUqkUZWVl2LJlCyQSCXJyckjVxfXr11N5OLdu3aJchEuXLkVOTg6qq6vJlp+fj7Fjx/LCiFOnTqU++/r6UoOlVCrF2LFjqeXCTk5OCA4OFswxEALrzuzo6ICuri4h1MuXL0dJSQlZhq1cB4I7ICiTGIlEgt7eXsyZM4d3vlOnTvHanDoYGRnB0dERwGANFyMjI+rZxcXFkf8r4+rVqzA1NaW8an19fdiwYQM+/fRT+Pr6Ij8/nwo7VFRUQF9fnyyjl0ql6O/vR05ODln1AoCKQdvY2FDVOLOysqhw0owZMyAWi3Hx4kXMmTNHMBHv0KFDggR66tSpJB+KjaM/fvwYhoaGyMzMxIMHD6hQNndbsGCBYEE/mUyGpKQk3Lt3D4aGhggICFD1+Cl0d3djzpw5yMnJweHDh0mdkJCQEEyYMAG5ublwdHSkyMBw+3d5eTkvBMASuIGBAbx8+RJdXV1kOa4yEhISMG/ePMHZO1sKYShIpVIEBQXB1NSUWiorhLNnz2Lq1KmCE1GWeAl5OIHBCerq1atJ8Thg0Ibv3LkTq1evRnp6OuW1F4lEvJwY1tbU1tbiyZMnqKqqwueffy7oidbEE6NKauHcuXMwMTGBXC6Hjo4O9b/GxkaKtAPD7+cAqGXKUqkUmzdvxjfffINz586prfr9c7RLYDDMxSUx3t7eSEhIgEwm40Undu7cSbXBlpYWBAQEwNDQkBoHKyoqsHHjRpUeYmBoEtPc3ExNcgAVJIabWMPdzpw5Q9a0s1tAQABcXV1JA2RXuPj6+sLOzg7e3t7UQ5PL5Th79iy2bt1KnTM+Pp6X7PXkyRPY2dlh/vz5+Mc//oGxY8fivffew9atW8msKykpCRYWFiqNGzefhy17DQySH3d3d8H157m5uXj//fcxfvx4latApFIppkyZgq+++grfffcdtU2ePBlz5szh5ckkJCTA3t4eAGBoaEiRp7q6Ori4uODNN9/kzSyam5spEjNp0iRqNgAMFj2ysbFBa2uryvLTJSUlWLFiBfLz83lbZ2cnent7ceLECfJM+vr6cPHiRYSFhZF9EokEenp6MDQ0xLJly6hCWSkpKZT3IC8vD87OznByciLb4cOHERcXx7u2zz//nPpcVVUFfX19LF26FN999x2WLFkCKysrXuxf02S7sLAwqpDhmjVryKwrKiqKJFHeuXOHVymS+5lNUh8YGEBRURFu3LiBDz/8kKwQYNHV1aVytqEKdXV1xPiUl5fD0NCQekfnz5+nPDFyuRwJCQnw8PDgFXfkIjY2Fh9++CF0dHSotlFSUkItwe7r60N4eDj++Mc/qvRYsPkrLDIyMnhGXRV6enoQEBCgspDayZMnieFja6f8+OOP2Lx585CTlGXLlvHq4bCoq6vDjh078NZbb/FCp8pgGAYREREYM2YMLC0tBcvhx8XF4dNPP8Xvf/976njD7d9D6d6kpaVh7Nix+Nvf/qbyO6rsk6pcPmCwb8XGxuLkyZM4fvw4bt++rdEKQJlMpvIZZ2ZmwszMDL/5zW8o/aaqqipim1QVyrt+/TomTZoEXV1dcvzExESqKrODgwO5V4ZhEBcXh7fffpvk+wGDA29ERASSk5Oxc+dO+Pv783JiEhIS8N133+HKlStU5V+pVAqxWAwfHx9cvXqVPI/du3djxYoVMDY2hrGxMRYvXkzJEYyknwMgSfe3b9+Go6MjXrx4AYVCgcDAQMyZMwcmJiaws7Mj7e/nbJfAoAdWX18fXV1d8PDwwDvvvEPSAHR1dbFs2TIYGxvDwMAAixYtEtQcDAoKQnd3Nx4/fozg4GCcOnVqSI2u6dOnIz4+nizuSU5Opur3XLx4EX5+fpQNeqUq1t3d3cjMzBxyqZ4y+vr6NEqMVSgU6OzspGYCwxGU4kJd0TBgMEdguMlaQ6Gzs5MwVlXL54SeXW9vL2X4R1pUrbOzU+XyNmCQQXONWXV19bCE1thzjATcrPqfAvfv36feOdf4c+9ZoVDw3sFQBl6dnthIMTAwwHvPyjPutrY2jYX72Gq1XHR0dAgO6qp0bITQ2NgoqMMihOrq6mFrSzU2NqrVhGEx1HcUCsWQfZ6L4uJitW2fYRg8ffqUGtRH2r/VoaqqSmV4Rx1UJXU/fvwYT548+ckEH7kVhBsaGpCVlaWxveK2DU1+IxQ+HKl22cOHDwVDdj8V2trakJGRIWj3GIbBw4cPBbXtfq522d3dTdm1oci/KpSXl6sdc5ShTs9PFV4pidFCCy200EILLbT4uaAlMVpooYUWWmihxS8SWhKjhRZaaKGFFlr8IqElMVpooYUWWmihxS8SWhKjhRZaaKGFFlr8IqElMVpooYUWWmihxS8SWhKjhRZaaKGFFlr8IvFKSIyQ8u+TJ094dVZaWlqGLA2uDurWm9fW1vIKPzU1NQ27zslwIZFIBCtDaoLs7GyNaxOwhdmGC2VRSE3xKp9nXl6eyvoxQtVGhSDUxkaDV3V/DMNoVOOIi7KyMkpba6R1f1RBlVAdC1XPvLm5WWXxtFeNn+JcmrYlYOhCcyxGo1wsVA5/KAy3nQ8MDPDqHCnbFHVCoABINW8WhYWFGhW/EwJXDFCLfx+8Cns3kqJ+w4VUKsWzZ8+G9RtBEqNQKBAbG0tJu4tEIiQlJeHChQvUPkdHR+jp6fGKzlVXV5NS7OxAmpWVRVWeVXcjxcXFSEhIwPHjx2FkZIQxY8bgL3/5C0+Gm4WbmxtsbW2pfQ4ODti0aZPg9+/fvw9fX1/IZDKUlZXh3r17vC0lJQUGBgZDGrzt27fD29t7yPtShqWlJa5fv67RdydPnkwEt/z8/KjrTE5OpnSFuPjkk09Iobze3l5epdPo6GjBhjyc58kwDGWwLSws8OjRI/LZ1taWKIsDNBmtqamBpaUluru7iQw9W1mTK+YpFouJ3AELb29vcqzi4mLcvHmTV6EzODgYZ8+eHdX9KaOurg66urrEKGzZskXt9/v7+2Fvb08qRqekpMDY2JgQoC+++GJYBebUoa+vD3/5y194RIZLIsPCwgTb6+nTpymF6p8SIzlXb28vli1bRt5taGgoAgMDyWc9PT2kpqaq/H1OTg62bt2K0NBQuLq6wtbWFoWFhWoL5enp6RHpEUdHR8HCfqrI08SJE8n/ysrK0NLSgjt37qC8vBzl5eUIDAzkFR9bsGCBoAacKnR3d2PdunWU6vGOHTtIZenW1laMGTNGZaVdYNAuc/WEjI2NSRl9hmEEJ1pVVVVwcnLiFah7//33BfXHRmNvR9PPtRjEaOwd9xjcZ1xfXy9YQFIqlcLOzo5whISEBIpL7N+/n5Kf4KKnpwf/8z//M6xijCo9MUIHuXbt2pBCV5cuXUJCQgJEIhGcnJwgEolgaWmJxMREPH78GPv27SM3ykVbWxvEYjG8vb3h4+OD+Ph4+Pr6Yvv27WhsbMSFCxfUVnW1srJCX18fNYPglpUWwo4dOxAQEID29nbU1dVBIpFg6dKlqK6uhkQiIRs7yDMMg76+Pt724sULJCcnU/vy8/Nha2urdkaze/dulaSMi6tXr8LLy4t8Xrx4MXV9Dx8+xLZt23i/6+/vx6RJk8jniooKZGZmIjs7G9nZ2Th//jw+/PBDQTXS4T7PmpoaHDlyBCkpKVi3bh1SUlKIgVmzZg08PT0hFotx/fp1nhK5q6sr0SthPXXl5eWwsrJCR0cH2tvbkZ6eztNIysvLw+TJkwEMNn4hYxsUFEQE9kZzf1zY29uTMt4AYG1tPWRV1bq6OrzzzjvEc8cKsllbWw+p7D4cJCQkwMrKCqmpqYiOjsbx48dx/fp1mJqaEq9CVFQUzp07x/utjY2NoCTET4GRnEsmk+HLL78kn5VVqY2NjXnlzx8/foyIiAg4Oztj7NixSEhIQGlpKXx9feHv74/+/n7MnDlTsB92dXXh448/JhO0hw8fYvz48VAoFIiKikJiYiLc3d0pIVCJRILc3Fzk5uZi5syZ8Pb2xsWLF/Hxxx+jsLAQ+vr6KCgowJ07d/DFF19QnouSkhJMnTp1SM+csk2JiorCjh07yOfS0lJiD9zd3fHy5UtIpVIi69Lf34+IiAgkJSUR6RhdXV0yyCxcuBDh4eEQiUQICgqiJhNc2NraUvIxFy5cUCtQOlx7y2I0/fxV4vz58xoLIA8XDMPg7t27KgWHR4vh2juFQoFLly5BJBLB3t4ekZGRCA8PR1hYGCwsLBASEoKwsDBqcsq9F65orrKQs5OTE27evCl43q6uLsybN0/lfdTW1vLe/7DCSdOnTxeUPVeGl5cX0T7w8vJCZGQkkpOT4ePjAxMTE4SEhBD1XGV0d3dj2rRphNQ4OTlBIpHg66+/FmT4rBfCy8sLYrEYurq6SE1NRV5eHhYsWEA6pqenJ4+Jsp2JCy5JUxapUygUuHz5MlJTU5GWlqbRxs6AS0tL4e7uTnmxVqxYAR8fH2rfqlWrqJlhR0cHDhw4AIVCQUo/K6uI5ufnU41EJpNBIpEgMzMTGzZsgEQigUgk4hk/a2trXrhpNM/zzp07GDt2LPr6+tDf30/ExaysrBATE4PKykr4+vqS7/f29oJhGEilUshkMqxbtw719fWIiIjAy5cvidL2d999h3v37gkKParSY6mqqsLAwABPVXc09wcMut4nTZpEuWafPXsmqCQMDLpgWTLn7OwMsVgMd3d3rFu3DgEBATh27BjEYjHOnTunUql3ODAzM0NRURGAQaPr7+9P/hcdHU28qUIk5ptvviGhMbavWVhYkOs/cOAAjhw5MqrrG+25/v73v5Pv7Nu3D9u3byefv/rqK0ENFxbcvh0aGkq8UQkJCYLK6mFhYUS/5/jx4ygtLeXN9pXVf+VyOZ4+fYrm5mZ8/fXXaGpqwowZM5CVlYWamhqiLRYSEoKrV69Sx7K2toaTkxPRjFmyZAmeP3+OjRs3knYZFBQEY2Nj6ncymQwtLS348ccfce3aNYjFYiQlJcHMzAxnz56FWCxGdHQ0XFxceB5zhmEopXAAMDU1ValvxUVxcTFFKrmaQkLteLj2lovh9vOfAtevX1fpQRgtenp6cOrUKbzxxhuv9LijtXfA4HvhkuTly5cTsWFAOM1j1qxZ5G8hEqMqBaO+vp6IyAptR44c4Ykya0xikpKS8MYbbwh2dmWsWrUKmZmZqK6uhr+/P6KiogAMuhQ9PDyQnZ0NZ2dn8n2FQgGxWEwe6PLly3H+/Hm4urrCxMQE+/btQ3R0NPHqHDhwgLouZcn2gYEBWFpaUrOcEydOUN4M9rzA4MwkMjISly9fxuzZs8kD++STT1TqrdTW1lIKw+x269Yt2NjY8AhDYWEhJY4IDBot5RngzJkzyW8ZhkFUVBRkMhni4uKwcOFCyOVyLFy4kCI+wcHBVCPp6uqCSCSCjY0NHBwccO/ePXz22WdU7saKFSsonZPRPs/q6mrs2bMHq1atQl9fH3p7e/HgwQPk5+djy5YtCA8PJ0KGrAtdIpHA1NQUHR0dKCwsJCRm6tSpKCkpwf79+1FQUIBdu3apJDHszO3cuXNITk4m7y4wMBD37t3jGbfRtBcA2LRpEx48eMDbv2XLFkHPQn19Pfr7+yGTySCTyWBubo7ly5fDxMQEly5dQm1tLfmfTCYbcS4CMOjN/POf/0xiyps3b0ZFRQXq6+vx8OFD+Pr6Ij09nSIxXLHXcePGkTY1btw4SKVSSsU3KCiIJzw6HLyKc3FneEKeGG5YhWEYKnQ6Y8YM8ndISAicnJzI5+vXryMoKIg615QpUwg51dXVFcz3UCYxXCxZsgT+/v7w8PBAcXExiouLiQ3Yu3cv9a7b29thaWkJmUyGOXPmkPagUCiwcOFC8j2hfsAwDJn4tLa2IiMjA5mZmfD09ER1dTVevHihUqHazc0NDg4O1L09ffqU8gaxiuLAYLgyMjKSeHGuXLkCkUiEkJAQhISEQCQSwc/PD1999RUvn2g09na4/fyXiKKioldOYkZi75Q9dSKRCCdPnoRIJMK5c+cocejw8HB8//33vBDguHHjyDvy9/eHkZER+bxu3TqVJObFixdYtWrVsO5RIxLDxu137dqF0NBQlJaWIiYmRuX3AwMD8ejRIxQUFCAhIQFJSUnIycnBmTNnEBMTg7y8PDg6OlK/aWpqouKrLi4umDx5MmxsbHD06FGVCbDJycm8l8TOSLjw9vYmrsCKigpER0fDyMiIcmvdu3ePYqNcCXBlPHz4EHl5eSgoKBDclGc8L1++1IjEqArXLVq0iMS1la8rPz8fe/bs4f3GwsICJSUlaGtro2TVAWDq1KmC5xnJ86yrq8PFixehUCjg7+/PUzvfvn07/Pz8IBKJkJiYiOXLlxOj6+HhgfDwcMTHx8PU1BT19fXQ0dFBeXm5ShLT0dGBiIgIeHl5Efcr1wXNMAwRLxPyxAz3/lhERESodCc3NTVh/vz5sLe3V9lWDxw4AB8fHyQnJ8PJyQkNDQ2wtrbGnDlzEBwcPCoCw17zjBkzUF5eDrlcDkNDQ5JLtGPHDpw4cQL37t0T9MRUVVVh/fr1AAYJqZmZGQBQuRKvaqAY6blkMhkmTpxIcko8PDzg4OBAPn///fe83I/ExETcv38fmZmZmDBhAlG1d3V1hbm5OTIzM3Hnzh1kZmYiKyuL2CCxWIz169eTsJ+enp5gwjSXxCgb/x9++AFLly5FUlISwsPDERMTA1NTU3R2dvIIWkhICAlJzp8/n/ofq3gM0CSmoqICUVFROH78OFEjLysrIyErdjt16hQV9mHR29uLKVOmoLy8nBqY2M3W1ha+vr44dOgQZc+GK7I4Uns7mn7O4u7du2TwZD3UYrGY5AwxDIPU1FQEBQWRBGiFQoFbt26hoqICsbGxZLJXWlpKcq7q6+tx+vRpBAUFUd4/VftVHRMY9NYGBATg+vXrFIlRdSyGYZCWloaoqCg8ePAAMplMZV7WaOwdMNi+2fAod0tLS8OFCxcEzwkMTsZZDMcTc//+fWzevFnlcYXyN4ckMQzDYNWqVYiLi4OPjw+50YyMDKxevZoXjmhqakJERAQSEhJw/vx5LF++HM7Ozujr68OkSZOQkZGB8vJy7N69W+U5PT09YW1tTRKHy8vLsXDhQsGkPbFYzHtJjx49QmFhIUW0lJOSAGDr1q0oKSkheSwBAQEICwsjn5cuXQqJRAI7OzuNV1L09fXBz8+PNyBxZ2EsNCUxly9fhr6+PvnMemLYWVBwcDDl7mOxYsUKAEBqaipsbGzIbLC4uBiTJ09GcXExnj17hk2bNhEmPZrnyTAMFi5ciGPHjhE3NTdPqLe3l9d42VBSRI1UHfkAACAASURBVEQE1q9fj/r6eixZskQtiWEhFovh4eEBYLCz7d27FyKRCO7u7jAyMgLAN24jvb9bt24hKiqKJB77+vrCzc2NGMhFixbh6tWrMDc3x5dffkl5BCoqKmBhYUGSJZOSkhAYGEj+//z5c3z88cdISkrivUNN0dnZiRMnTmDr1q0oLy9HXFwcuf7du3fj/v37aknM1atXERwcDAAQiUTkuf4UJGak5+rt7YWenh5pw25ubjh48CD5vHXrVrWrH7l9SywWExt0/PhxWFpakv8xDAMXFxcqd0kTEsNCKpXi+fPnMDY2Rnd3N8rKytDV1YWmpiaYmppCLpfD2dmZIgPc1YeLFy8GAFy5cgUMw5DPgLAnhtsPqqqqsHjxYsoF7+rqSnmsWNjb25N8FzbszTAMIYJC9mk0GKm9HUk/Z1FcXIzf//73VFh1//79hETY2tri0aNHyMrKwn/9138hPj4e58+fx3/8x3/AwsIChoaG2L59O54+fYoJEybA3NwcDQ0N+Oijj1BbW4uEhAT89a9/hVQqVbkfgOAxAeDgwYPw9PREd3c31q9fT0iMumNZWVkhLCwMMpkMCxYswGeffUaF6bkYjT0HgHnz5pG0AO527949XliTi6FyYlTZuqNHj1JeG+Xts88+4+XxDEli9u3bR26OS2KAQZY3duxYEoPnorq6Gh988AGio6ORmpqK0tJSrFy5ElOmTEFDQwOMjIzQ0NBAXVBPTw/s7Oxw6dIlAIOxajZ0VFlZiRkzZmDOnDk4efIkcTumpqbC3Nyc3OSsWbMIW9PX1yerZJydnXnMcevWrcjNzcXly5cFs+W5G1eWHBgkFvHx8bzZS1JSEuLj43nfLyoq4oWBDAwMeDkx48ePp37X3NyM3bt3U6u62BlLREQEgH/mzXBXoHR0dJDM82PHjiEhIQF5eXlk1jp16lTyd3l5OfEejOZ5hoaGwsPDAzt37gQAzJ07l3qGsbGx+PLLL3kEj/XYsSRm2bJlKC8vh7W1tcYkJjk5mfydlpZGvqts3EZyf+3t7byYvK2tLW7cuEE+6+rqkng/e7ze3l5cuXIFsbGx1AzC3NycJFmyaG1tRWNjI0aKsrIyKBQKQmKOHj0Ka2trPH/+HPPnz4dcLldLYvbu3UtyEtgBHFBPLJqbmzFx4kQy0GuKkZzryZMnxIvCbvb29rCysqL2nTx5EnZ2doLnnThxIqKjoxEfH4/79+/D2NgYra2tGDt2LLVo4OnTp2hvbx8xiTl+/Dj09fWRlJQEsVgMIyMjnDt3Ds3NzWQio9yeY2JiiD2ZOnUq4uPjERQUhMrKSuq5DNUP6urqeJMlkUjEIzH379+Hjo4O8YLp6OiQFV9s+GokJEYul+PQoUOCie4jtbcj6edcWFtbE7KjUCjImJKZmYnvv/8e8fHxiI+Px7x588iz/uCDD3hJqzY2NjA3N0d5eTmZNLa1teG1115DbW2tyv0slI9ZWFiIDz/8kJDZnJwcQmJUHau/vx9vvPEGyVkKDQ2lFm8oYzT2HBgkMUJkIjw8HLt27RI858DAAD7//HPy3Rs3biAiIoJ8Xr9+vUoSU1NTo3a14Lhx43j71JIYd3d3Ks6vTGKAQVbFzSY2NTXF/v37cfXqVUyfPh0KhQIymQxmZmZoampCeno6BgYGsGzZMvj6+pKZ048//oiTJ0+ira2NDHKsu4yFRCLBmTNncOzYMeLevH37tmDMDxhcxhkbGwtgkPEqN3LW4AODrjtPT09cuXKFbEFBQTh48KDgs3nx4oXgKoK+vj6kpaXx9hcVFVGzbwDYs2cPz0hwE+xkMhmCgoLQ3t4uSGK8vLzQ09MDBwcHuLm5UV6xmJgYkoS2ZMkSXqKdqrDVSJ/nw4cP4e3tjZqaGqxZswYAqMQxNp4qxN737t2Ljo4OmJiY4Pnz59iyZQvpxAUFBbC0tBzSeKemplLGjV1yrmzcRtNeuFi4cCFFOubNm8erhZOfn4+YmBge0X3vvfdw9epVap+fnx8WL148rKWFQti6dSvEYjEaGxuRm5uLU6dOYe/evQAGl+Yr58Sw+Oabb0i/09XVJfeijlg0NDRg7Nix1EotTTCSc1VWVqKxsZHksBw5cgQfffQR5s+fj+LiYl7pAIZh0N3dTcIiq1atwhdffEHONTAwgIkTJ8LIyAgXL14UvM7hkpiuri6cP38e7u7upC0Cg4Nofn4+GhsbKYIxfvx4ng3p6enBjBkzKC+Nsifm2LFjvOtkz1dbW8ubLB09epTK7yorK8OZM2eQkpJC7o8lLvn5+aSUgSoS09vbi7Nnz1I5E+wWFxeHU6dOUSUWWIzU3o6kn3NRWlqKX//616ipqYFYLCb2+cSJE9i2bRvlYWDf8ZgxY3he4wMHDsDc3BzAIFn08PDAyZMn8dprr5Fwj6r9Qsf08/Ojxs7i4mIqnCR0LJlMhrfffpuE5UJCQkgoUQijtXezZs0SJJnnz58X9P4D/6z1wpaNyM/Pp0qJdHV1qSUqykhOTkZlZSV6enoExy1BEtPf34+TJ0+isLCQ2i9EYgYGBngJkBUVFfDw8ICOjg4UCgU8PDx4GcxPnz7Fp59+itLSUnR3d6OxsZHMZA0MDPDy5UuEhISQmCnDMMjOzsb27dupnAMhdxmbOMqd8e/evZu3sortVI8ePUJubi5vBqPsBuPC3t4ejo6OPIZ68eJFjB07VqPwkxCJ4aKlpQVyuRwSiUSQxJSWlmLt2rU8rw8wmIMTGBiIw4cP8/JhANUkZiTPs7KykiQOcnMcuLF89n9cElNbWwtra2tMmzYNwOBS6wsXLiAkJAQSiQSlpaV4+vQptm3bppbEvHz5EmFhYdiyZQvEYjE8PT1J4rgm4SRN2wv3PrjhPQD4xz/+wfMwSaVSXqz69u3bWLduHdLT0wVX23HBhqxCQ0PVfo8L7kABABs3biQJfO7u7rh165YgiYmMjIS/vz/CwsLw9ddfk/0/RThptOe6ffs2vv32W4SEhMDNzQ0bNmwQTLrt7e3FypUryaxPuc3Pnj2beA0B/oqakXpiUlJSSFtkPTFPnz5FQ0MDZWMmTJjAS4hMS0uDo6MjrKysyKREmcQor9riDvLcFVAsRCIR3NzcyGd2EOOSGLav5ufnE9L7U4STRmJvR9LPlaGrqwtHR0c4OjqSfhoUFIRZs2ZR/Zb18I8dO5ZHYg4ePAhzc3MUFRVh0qRJaG1thUwmIwRD1X4Wysd0cXHB559/Tj4XFxfj9ddfBwC1x0pMTMT8+fNx7tw5MuFThdHYO4Zh4ObmhvT0dNTX1+Pw4cMoLi5GfX098vPzKU80F2fOnAEArFmzhtyvgYEBIUwNDQ2wt7cn4XW5XI6oqCgqYZu7nT59GsnJycjLy4OhoSHvfIIkZmBgQDB5y8vLS6N18mVlZbh27RqSk5NRVFREaqmwBdFqamoE3Umsm4/tUHK5HJ999hn6+vpIAShXV1dqIBRKXBJa3rdx40ZeBc0tW7bg6NGjiI2NRX19vWCnUlX3wNHRUXD1BJvToQnYXIWhoIrEAICdnR0iIyNV/nbTpk1YsWIFDAwMKE+BKhIzkufJrTKal5dHDNHChQupxnjhwgWeJ6a4uJhqmObm5njy5Al8fX2hUCjQ09OD7u5ulSTG0NAQ7u7uyMjIIJ2wqqqKEHBNEns1bS8s9uzZw1uhJOTmVIZMJoOOjg6p1uvk5KSyRszAwACOHz8OYJB8aAouibl27RoxKAAI2VW1xJr9/axZs2BiYgKJRIJZs2aR97dr1y5iiIBBkrBz5061BeaGulZNz8UiNTUVa9asQW9vL06fPg1vb29UVFTgyy+/hEgkUns+ts3L5XIcPHgQW7ZswbRp00i/UF6sMFISww15AINkICcnh9iYCxcuYMKECTwiDADr1q1DeXk5njx5gkmTJmFgYABfffUVeS4+Pj6UTXr58iVsbGxw6NAhdHd3o7y8HEuWLOHlxPj5+fHOlZycTHJi5s2bR/Lr2H2vmsSM1N6OpJ8rIyUlBX/6059ILhYw6E1//fXX4eLigoGBAVKAEBj0mijXMmFJjKenJ/Fc1dTU4LXXXkNZWRlcXFwE97P2RfmY9+/fx2uvvUYq4T5//hyvvfYapFKpynP09PTAxMRE48rIo7F37LP6xz/+ge7ubojFYlLeo6KiAnfv3oWjoyM1UWtpaSHtev369STs9fDhQ5w+fRrAoPeopaUFf/nLX3jFHtUhKiqKV/AUGGadGA8PD1I3QR242cUmJiZktqGrq0ti4T/88AMSExOpY7MFmhYvXkxYIut1CQwMxJMnTzAwMECtExeJRLyYH2uUamtr8eTJE1RVVeHzzz/neSyMjIzIKqna2loYGRlRy6Xj4uJ4q6hYODk5ISIigufGLioq4nkgVIHN9RgKHR0dFInR0dFBcHAw/P390d7ejtWrV8PAwIBauw8MxubZZ3zo0CGkpaURcsrNHudipM+TfV+hoaFk4OTOIIFBo6O8fC4lJYWQmJs3bxKvQ2RkJHbt2kWuNz09nQzqLM6ePYupU6cKZqyzbnp/f3/KuI2mvQCDuVBskiBb54bNd1KHnp4erF69mqozIZfLsXPnTqxevRrp6em8FW1OTk4IDg5WuYRXCJs3b0ZJSQmysrJ4VXnZJdyhoaGC9S68vLzIYOft7Y0HDx5Q7u6goCAq9FJWVobXX39dcOXLUBjuuRiGIW2ebRMhISFUGGX69OmYN28eQkNDecaxu7sbc+bMQU5ODg4fPozc3FxyjAkTJiA3NxeOjo7U5I07yOvq6lLhQzb84OnpySOZIpGI54l58OABqqurycD97NkzXijp7NmzVMh5zZo1KCwspLxV9+7dI9dUV1dH2m9fXx/279+PwMBA5OXlUbNstn9IJBIqXCkSicjEMSAgAMBgm2QHJTbfaCR4/Pgx1X+BkdvbkfRzZTAMQxFWFgEBAfjVr36FX/3qV5gzZw46Oztx8+ZNvPnmm1i9ejW15JslMY8fP8Zbb72FpUuX4tSpU3jnnXdgYmKCBw8eCO7v6elReUx7e3v87ne/w9q1a/HDDz/g17/+NXx8fJCTk6PyWLNnz8Ybb7yBP/zhD3j33Xcxe/ZslV7dkdq7y5cvk4ra3LpkLIFkCwvu3r2bKnmir69PiKipqSmpbcbFwoULIZVK4eHhMWTEoqKiAi9evEBLSwtJT1DGsEiMi4sLxWSFEBwcTMIJwGAIinU73bhxgyTtvnz5kmLdx48fJ+6y7du3Q19fH+bm5jA3N8eGDRvwwQcfECbHRWJiIkWsHBwcyINhGAZxcXF4++23sWHDBt5v6+rqSCcrLy+HoaEhqWWSn5+P8+fPq/TE2Nvbw8XFhZdsyLr6NMGOHTs0msU2NzdTJGbSpEk8L1BoaChsbGzQ2tqKnp4enDx5kteApFIpIiIiMHnyZCxdulTwXCN9npWVlVi5ciVmzJhBvs/W/ejo6EBQUBDmzp3LWwrO1hK6desWaRvAP2fLHR0dOHToEP70pz/xEs9kMpnKcuqZmZkwMzPDb37zGzJgjeb++vr6cPr0aaSkpJB99fX1sLCwwFtvvaXyPVZVVZF3o6pg1/Xr1zFp0iTo6ury7me42j0bNmxAbGysoM5JU1MTnJ2d8eabb1L6JOXl5fD09OSRYADUoFpeXs5L2CwqKhqWBtJIztXb24vLly/zCtkFBARQchtyuRxeXl64ffs26dcMwyAiIgJjxoyBpaWloLxDXFwcPv30U/z+97+n+kxCQgLJ9zE0NKRyzurq6uDi4oI333yTN4NNSEigQuwhISEoKSlBeXk5z/vAXvfZs2d57vnW1lYAoGafZWVlVIVtZSJ069Yt7Nq1C99++y3mzp2LiRMn4qOPPsJ///d/Y8mSJdQMPiEhgVRQ56KkpAQ7d+7Eb3/7W2rhBbvkWdXKEe5248YNXLlyhVotNlJ7O5J+LgRVGmcSiQTl5eVDljg4cOAAeRfd3d2k3UskEkL8VO1Xh+bmZjQ2NqK/v5/y9gkdq6urC4cPH0Z3dzcqKyvx/Plz3Lx5U+Vq35Hau7Vr15IVXFOnToWFhQV27dqFXbt2wdLSEh9++CEqKyvR1NRErWrKzMwkZPPYsWOYOnUqDAwMyDZ//nxMnjxZZT0gZUgkEri5ueH111+Hrq6u4HeGRWJaWlqGfNEVFRVUnLejo2NE4n1seWl29qCqFLcm4nmaiE4ODAzwjqWuAWZlZQnO1Pv6+jQWsMrLy9OoQmtvby9VQXOoe25tbVX7nqqqqlTWMhnN82xoaKDe9d27d6n/3759W/B+5XI5tSRZCNnZ2UNelxCUC/qN9P5evHghOBOUy+Uq5TAaGhqQlZWlscDjcOtvCKGkpGTIPsrNT5PL5ZQY5U+JV32uoqIijVd0FRcXqxW7YxgGT58+pQbLzs5OYgNUub2Fzt/a2ipo89rb2wULTArZnp8DDQ0NKu0AwzBE6+unwHDt7VAQeq6vCgUFBWAYBrt370Z8fPxPdh5NcOzYMZibm1Ne27S0NJUSHq9ifOzu7qaSnxsaGjQWjuzt7aUkJZS9zZoiJydHpX18JSrWWmihhRZaaPF/DVKpFB988AFWrVoFb2/vURekHC3Ky8thYGCA//3f/8X06dNhZGQ0LMHQ/4vQkhgttNBCCy20UIN/NXkRwr/jNf0roCUxWmihhRZaaKHFLxJaEqOFFlpooYUWWvwioSUxWmihhRZaaKHFLxJaEqOFFlpooYUWWvwioRGJqaqqopZNNzY2Dqs2hBYjR319/f83z3o0y0yVy7erwnDrrvyUGO6S0ry8PF6xruEeayTlDv5/xXCX7TY2NlLL1wsLC0ecfKlpRdbRIDs7W+USa2XI5XLBexnNsmhNkZWVpfF1sqivr9eofMW/CkL9WFMb9kuB8jtTljESQn5+PrV8u6ysbMi+oBGJOXPmDFVJ8cqVK6S4jkQioYo93b9/H76+vpDJZCgrKxMUj0pJSYGBgQHvpXl7exMjUFxcjJs3b/KKKAUHBwtKhv9fRUREBCWKplzwSyKRUEXUZDIZTExMeMJsytuePXsEiwcOF8+fP0dycjJSUlKQkpICsViM/fv34+nTp/Dy8iLv7fz580R1WxXs7e1hYmKisWHMycnB1q1bERoaCldXV9ja2qKwsFCtuJienh6p+Ozo6Ej0urgYiWHu6OiAm5sbZeiTkpLU1jEZN24c9T4ZhqH6koWFBSWkZ2trS6ngcgfMmpoaWFpaoru7G56enhCJRBCLxUhISCAVXgGQ96OFejAMg/fee29Y9YmysrIoyRFjY2OiD8MwjOBAXFVVBScnJ6oGhlQqxfvvvy9YhXU09lUZlpaWlDCfOtTW1kJfX59nRzZu3EjpMr1qyOVyjBkzRlA2QR02bdpExBqHwr9i3NHX10dUVBS1b9q0aSgpKcHLly+p/T09PSgvL9doe/bsGRwcHF5JzanRYu/evZR0yIwZMwRrq3Exe/Zs0meAQeVw5crjylApABkREUFUSm1tbbFv3z7ScA8dOoSdO3dCJBLB398fu3fvppjljh07EBAQgPb2dtTV1UEikWDp0qWorq6mCt8oF8zJy8vD5MmTAfyz2J0ygoKCSMljLp4+fYodO3aoLJp24cIFSq5AHcrKyrB///4hDRjDMLh79y42btyo0XFHgosXL1L6N9u2baOqZtrZ2fGqLbKCiurg4eHxSsT8urq60NvbC5FIBB8fHwCDxrumpoYqxW9vb89T3+WiqqoKf/7zn4dk648fP0ZERAScnZ0xduxYJCQkoLS0FL6+vvD390d/fz9mzpwpWC69q6sLH3/8MSm49PDhQ4wfPx4KhQJRUVFITEyEu7s7TExMAGheoXTatGkQiUS4du0aVXGTLTcPgOjFcDFmzBjeNdbU1ODIkSNISUnBunXrCDEUi8VYs+b/sXfeUVHd6f8n3V/KHrPZ3bglRjmbxDXBGrFFl9hjTQRFTcSIghKaCkpRPGKhiVJEOtJFQBQYYIAJwYZjQ4oRRDoIAxZ6n2Hevz/m3M93LvdOYcRs3L2vc+Yc5jJz7517P+V9n+f5PM9GeHp6QiAQIDk5Ga6urrQ+5OrqiujoaGzatIkcq7q6GtbW1mhra0NraysuX748rDIGmhAbG6tWjTVN+C36HABcu3YNGzZsYGwvKysj48LQcdLPzw8rV64k4+TChQtx5swZ8Pl8BAQE0MSkPHZ2drTSDXFxcQozhQOaj69D2bVrl9plBZqbm2k12yj27dunVukUTQkJCcHWrVsREhJCMhiroqKiAh9++CHS09ORm5tLXk5OTqz10J5n3tGEvr4+TJs2DRKJBIODgyTB4ldffQVA1h7k6+H19PTg0qVLKC0tpb3mzZuH8PBwxvbS0tJh1SQaaaiH6qFzqLzAj4+PZ7S9x48f00rViMVi6OjokP6VnJzMWuVcLUsMVTDu8uXLaGhoAI/HU1p+gOpc8sgXHKTqJ7GhKDV7XV0d+vv7WQt9SaVSZGVlQUtLS+H35UsesCGRSMgEdPv2bbzzzjsqC8p1d3cjJCSEVj59pImPj0d4eDjJDGtubk77jXFxcTh16hTtO7Nnz0Zvby8ty6JIJEJDQwOsra3R3Nw8YiKGYqiIaW1tpTVaqg4HG1KpFF9//bXaIpNCvk0FBwcTxZ6Wlsa4JgAQFhZGUnC7ubmhsrKS8XTFVtBvOOchz4YNG0gxxidPnmDq1Kk0IfPJJ5+wfu/SpUvQ1tZGb28v+vr6yP2ztrZGUlISamtr4e3tTT5P1XAaGBiAWCzGDz/8AJFIhMjISDx8+BD79u3DvXv3sHz5ctZCmiNNcnIya22mkeBF9Lnu7m5ERESQwZLP50NPTw/BwcEMy0NISAhrVXGpVIqCggL89NNPZJuRkREt07YiysvLoaurS95bW1uTJ2k2l4gm42tlZSU8PDxov+W7776Dl5cXbZuhoSGrJbOlpQW6uroMAW9oaPjCRExNTQ3+9a9/oa2tDb29vTAxMVHpIpJKpVi+fDlu3bqF3t5e8nrw4AHGjBnDqExNocm8oynx8fGwtLQEn8+Hm5sb5s+fj8HBQfLQ19bWpjKx3sDAgNIx9T+Jrq4uOjs7YWdnh9OnT8PJyQlpaWlkPoiIiMC6deto5TMAWYmi9PR0ODg4kIrlYWFhxBq9bds2+Pj4MCzlaokYkUiEzs5OODk5ITw8HB0dHRCLxQpTCFMdsK+vD1FRUUhISMCcOXNIw//kk08U1k6gniCio6NppblPnz6Nq1evKm1Mr7/+usLGqIrY2Fjk5uaS9+PGjVMpYgDZk9mLEDEJCQng8/kwNTWFra0tEhIS4OXlhZ07d6oUMXPmzEFXVxeys7NptUmo10iKmIaGBgiFQnh5eZGCcQsXLkRrayspHFZaWqpUQLq5ucHc3Jy8t7e3h4+PD8MkKpVKacU2Z86cSf4OCgrCwYMHyfvk5GQEBATQvj9t2jQcOnQIgKygH5uvlU3ECAQChISEkIHezc2N5tZRJGI2bdpERAwgM40ePnyYvP/0008Z36mvr8fu3bthaGiI3t5e9PT04Pr167h37x5MTExw5swZch+pp63Ozk4YGRmhra0NDx48ICJm+vTpqKiowL59+1BaWkoKjr5oEfOieVF9jqKwsBAmJiYAZPV+1qxZozJey93dHQcOHKC1qeLiYtr35N0Evb29iIqKIsIpMTERfD4fQUFBCAoKAp/Ph4+PD/79738z4rg0GV8fPHjAqNvEVqV61qxZrJPn06dPFVpi2Op0PS/d3d1YvHgxioqKcOfOHTx9+hSlpaVYtmwZqwuYwsXFBT/99BOtlll3dzdmzZql1HX2PPPOcJBKpfjmm29w5MgRiEQiuLq6EvfvjBkziBvY2dkZ1tbWtO/6+vqSMcjHxwcBAQEMkb1///7/SAkLeSgx5uDgQLPELFiwAEFBQcQaJq8fJBIJ/vnPf+Ls2bOwtLREUVERecA8fPgwzMzMWKvaAypETH9/PzZs2EAu0A8//ICjR4+Cz+eDx+NhwoQJNL9rTU0Nzp49CwMDA1rJ8atXr8LOzo68H9oZKLP9iRMniJlY3hQqlUqJL01ZY3rzzTdRUlKClJQUREVF0Sq2lpeXk8JwP//8M2pqanDhwgUUFRUhJycH7777Lq0wnba2Nvh8PgoKChAQEEBzEwCyqsp+fn5ITk6mDagikQihoaEICAgg8Q6XLl0inQKQPTkLBAJcunRJ2eUHACxdupRWGM/S0hJnzpwh+3N0dGQVMYDMNSF/Derq6kiMxki6k2pra5GQkICTJ0+is7MTa9eupYmYmJgYhXEmiYmJ2LBhAxmYa2tr8f/+3/9jFMOjSE9PR15eHoRCIXR0dEjhTVdXVxgbG0MoFOLSpUsQCoW4efMm2a9AIMDmzZtx8OBBALLYmKH3FGAXMWfPniXVkgHZE4O8KFNXxDx+/Ji4OwcGBjBp0iTa5xsbG3Hu3DkMDg7C19cX4eHhiIqKIv1v586d8PHxAZ/PR3p6Or799lvy1Hz8+HGcOXMGPB4PRkZGEIlEWLBgAaqrq9USMVeuXCFtSiwWo7OzEwKBgDxlS6VS5OTkICAgAMXFxQDA2pcA2VM/NYmw9Qdl2xXtExhen6POOTc3FzExMbh+/TrEYrHKeKeBgQFa22hpaWGdvOXp6enBtGnTUF1djYiICMbEYmdnB29vbxw6dIg2cA83bkHT8RWQCSh1RIyitiwSiRRaYtQpYquIvr4+xsNie3s7Nm7cSFyxHh4exBUnFAoxfvx4uLm50QpyAkBSUhKSkpIglUqxdOlSpKWloa+vD8uWLaO55ClGat4ZDlFRUbC2tkZpaSmWL1+O1atXo6OjA/39/Vi1apVSD4e8tY66x62trcT9DQD/+te/flNXEtv9o1xCQ0WMvKsIAK2gb0xMDIyNjcHn82FlZUUTOjY2NjAyMoKHhwerJU6lJWbpz3XHVQAAIABJREFU0qXkb1tbW+Tl5ZH38u4CeUxNTVFRUUFMeX5+fggLCyPvly1bhs7OTtjb29NW3ggEAjJZ8Hg82NjYgM/nw8PDAwYGBgBUi5jt27fD1NQUH3/8MaZOnYqBgQEUFBRg4sSJMDU1RWxsLF577TWYmZlBX18fO3fuxN27d/Hhhx/i9OnTpIqttrY2rKys4O7ujqVLl2LRokXkOI6OjvD09ERXVxc2b95MBtSmpiaMGzcODQ0NSEtLwz/+8Q8MDAxAKBTijTfeQHR0NNmHhYWFShHT2NiIUaNGwcrKCtu3b0d/fz8sLS3VssQAMoFgZmZGBlNbW1sygY20OykuLo4oZ8qdRHW0x48fIywsjPGdjIwM7Nu3jzaYb9u2Te1AQfkBVyAQkGBzNzc3WFpakv9JpVK4uLhAIBBoJGISExMZIka+iri6IkaeoTFD8kilUixcuBDHjh0jbgp50zglguWhXEmRkZHYvHkzRCIRlixZoraIKS8vx3vvvQdfX1+ybd++fURE2NnZ4c6dO7h58yb+9Kc/gcfjsfal4uJi6OjowNjYWGF/ULQdAOs+geH3OUDmlgkLC4NYLIaenh4+++wzmhtuKBKJBIsXLyaTJwBaOwbYCz7u37+fTLJUbKBUKiWxDmxi4XnQZHwtLy9/LhHT3t7OGk9SV1cHqVSK8PBwEpDJ5p5T9AoPD4eDgwOxNt25cweOjo60ANCTJ0/SYqxqamqwZMkSzJkzh1TZbmlpoY2LFRUVePDgAZYsWaLUCgw8/7yjLlVVVcjIyMCtW7dgYGCA9PR0Mk5duXIFjo6O8PDwIA8JQ5k9ezb5W5GIUXT/hsPz3D+AKWLq6+tRWlqKRYsW0ax8mzdvRkdHB7q7u3Hq1CmEh4cTESMSiVBQUAB3d3c0NTXhwYMH4PF4mDlzJvT19WkWT5UihnqaBmQ+XnmTKCVihj7dmJqaoqCgAAkJCazR8/Iv+cYq35iysrLI37m5uWTgVSViKEuKSCTC22+/jYSEBADA7t27SfDc2LFjae4Aapu8O0lbWxvnz58H8H8xMoDMLPvxxx+TiTc/P58MqNXV1cQt0tLSAi0tLfKkbG5uTgYRqVTKGFDYcHR0hLW1NcLDw3Hw4EFUVVXByspKbRGTnJxM+190dDQRUiMlYnJycnDx4kVYWVnhwIED4PP5mD9/Ps0SA8jum7wPNCkpiXEPUlNToa+vr/ay1MmTJ+Ps2bPg8XjIy8vD+vXr8ezZM2hra9MqSxcXF6O1tVVjEXPhwoURETHXrl0jlsvc3Fxa/IQ8wcHBOH78OCwsLADIAv7k+8yFCxegq6vLuE6lpaVIS0sjImbVqlWorq7Gnj171HIn7dmzhwzag4ODcHBwACB7+l2xYgV4PB54PB7mzZtH+j5bX9q7dy+MjY0V9gdl/YRtn5r0ub6+Prz66qskJiU4OBhTpkxh/d0UERER0NHRQVtbG7GonD9/npj5+Xw+Nm7cSJv48/LysGDBAmzZsgWAzGQuEAgQHBxMRKomIkYikeDQoUNoa2tj/E+T8bWsrAwLFy6kTUBr165lxMR88cUXtGMlJSUhIyMD6enpSE9Ph0AgQEBAALZs2UKzyMTHxyu0nqpLbW0tfv31V8Z2Ly8v1kBxRYHLHR0dyMvLw4oVK5Cfn0/7H1tw8PPOO+oi318DAwOhr69P7u+aNWtQV1eHxsZGzJw5kzUVgvw48yJFjKZQ7rGxY8fC0dERR44cQVJSEkk1EBQUhHXr1sHY2Bhbt26Fnp4eWU0qkUhoIubq1asktis8PBw6OjrkAWHotWEVMc+ePSO+udmzZ5OG+sUXXyA1NZW819XVhbu7O/T09GhuC1NTUzJ4i0QieHp6IjExkbwCAgJYo4zlG1NOTg6tMbm4uABQLWLkJ/ivv/4aR44cASAbWCkRM378eMaTLJuIocxkJSUlZND08fHBvHnzyOfKy8tppu3GxkYcP34c/v7+0NLSIubtmpoajBo1CtXV1cjJyaFFn7PR0NCAnTt3ksBeCjZ3kvzTM/B/DTklJQU+Pj4Qi8XkKV2ZiGltbdU4t8L27dvJUypliZEXMSKRCJs3bwYgsxoMDR6srKzEihUr0Nvbi5aWFtbAtq6uLmKeNzQ0xMSJE2kNe/LkyTAwMMC5c+dYz1FTEXP+/HmYmJiQa75z506aKVSViOnq6sKxY8doS2YtLCyIwJbn1q1bOHnyJB49eoSNGzcCAPT09GgTTXR0NNavX8/4ro2NDdra2rBp0ybcv38fJiYmZJIvLS2FpaWlUhFTWVmJN954A48ePYJAICD94dSpU9ixYwctSJy6dmx9ycHBgSxvVdQfFG1n26cmfU4sFmP06NHE7RIUFIQVK1aw/m5A5jJMT09n3Muhlhh5qqqqEB4ejuzsbNKuKOFy7949spRdkYjp6elR+MSbmpqKkJAQ2hJ7Ck3G17KyMppbGpA92A09L0XCOjMzE7NmzYJEIoFQKMSePXvI/yQSiVoBzJqiSMSw0draCmNjY+jq6oLH49GEVnZ2NsaNG8dwtzzvvDNcpFIpzMzM8MsvvwCQxVPJr3yyt7fH0qVLGeOTppaY5xnXh4NEIoGXlxexQCpCUa6roZaYnp4euLi44NSpU2hpacGSJUtY2xmriJH3BVKUlJRg9erVyMvLQ1lZmdKTpDrZnTt3UFBQwLA6DO0EFFRjoiKTqYnD09OTBEQOR8QsXLiQBHfu27ePiBhtbe1hiZjS0lIyaLq4uGDChAnkc+Xl5XjllVcAyAaKKVOm4NmzZxCLxYzBecuWLdi5cyfMzMxU+i2PHj2Kjo4OnDt3jrZqh82dNDSHAtWQz58/D3Nzc9KJ7ezsSG6YoSJGIpFg9OjRai3PHkpvby9tOSolYpYuXYq+vj50dnbi2rVrCt2PIpEIW7ZsoZkkk5OTsW7dOprq7unpwbp165CRkUH7nfK/m7JeAMyVHZqKmKExMf7+/khLS6Mdlw1DQ0OcPn0aBgYGNNFWXV2N6dOn4/DhwzT3BBVbBMgCfKmne3lXJvU/eRHT0NCAPXv2kHvn6uqKuLg4BAUFobOzE5WVlSguLsaOHTtUBvauXLkSTk5OcHJyIiIyICAAs2fPpolKKnCUrS85OjrC2NhYYX9Q1U+G7lPTPpeeno758+cjOjqaCDo2amtryf0Zjoih2qa8iKHu1b1792BjYwPgxbiTNBlfh8ImYhRhY2ND2qZQKISBgQEZV+Li4hjL/UcSdURMR0cHQkND4eHhgcTERIW5kNj6qqbzjre3N9zd3VlXqyni6dOn2LNnD27fvo3u7m74+voy9jswMICtW7cy4mNmzZpF/lZXxDzPuK4ppaWlSEhIYE1HkZ2djcWLF7OuXpQXMZmZmQgMDERzczMaGhqwevVqREdHo6ysjNGP1VqdRAW7UeLF0dER9+/fV/h5ExMTHD16lOTGYOtkbHkQBAIB9PX14eHhgWvXriErKwuAzO9K5Q9RV8R0dnZi3LhxZJKwtbUlKw7Gjx9PC4wD/m81EjWxjRs3jkyWJSUl0NLSglQqRV5eHrS0tEhE/v3796GlpYWBgQF4enqSJ7FHjx5BS0sLVVVVZDItLS3FqFGjYG9vr/DaAbInYmopbmxsLC0pnYWFBUPEDPXzU4q9oKCAFhj5+PFjci5DRYxUKoWzs7NG5khvb2+aq2jVqlXo6OjAokWL0NDQAHt7e7z11ltIT09nfLewsBCmpqbEzdLV1QWRSISHDx9i4cKFxL3BBnWuEokEjo6OMDExwZdffkmUflJSEu3zmoqYgoICVjP30PMYypQpU/Dtt9/SBOujR4/w1VdfobS0FI2NjZgyZQpx0coLtqKiIjIRLVy4kDYQxMXFMSwx5eXl0NfXJ++NjY1RWFgIb29vDA4Ooru7G11dXSpFTHZ2Nj744APaAFpSUoJXXnkFLi4u6O/vR3V1NXmqZ+tLlIhR1B9cXFyU9pOh+9S0z23atGnYmW+HI2IosrKySEzMvHnzwOfzERgYSLaNtIjRdHwdyq5du2gxjoro7u7GkiVL4OPjg5aWFrVF0kihSsS0trbi559/Jv3s6tWrwxYxw513+vv74ebmBkAWeKwO7e3tSElJIWPyzZs3iQejubmZWDCEQiFrosOZM2eSv9UVMc8zrv/WyIsYKmg7Li4O+/fvJ96hTz/9lKTyoFApYtrb27Fjxw6alaKvrw/r16+Ho6Mja3I5AwMDkuG3oaEBBgYGqK+vJ6/U1FRaBmCKiIgITJ8+nVXRU8F6bMqVwsjIiOQb2blzJ+mgFRUVmDVrFiZNmoTQ0FCMGjUKGzZsoC1D3L59O8aPH4/o6Gjw+Xy89dZbMDMzQ21tLY4cOQItLS3ipti/fz/effddfP/99/jpp5/wxhtvwMvLC/n5+XjnnXewbNkyhISE4P3338emTZtoFoFVq1YxMjIqIzIykjahmJmZMdxJQzMaKlPdNTU1aGpqwvbt28Hj8Wj/CwkJoWVLVIeioiKSxyYsLAwrVqzAjBkzAPxf8iaAPSDy2bNnePfddzF+/HisWLECFhYW8PLywsWLF5Gfn4+ysjKMGjWKNdCtq6sLc+fORX5+PpydnVFQUABA5jbQ0dFBQUEBnJycaEHD8pPNypUraedEuUk8PT3VGpSam5uRlJSEiIgImqtDnpKSEtJuJRIJQkNDoa+vj6qqKvKZ06dP0xKdUdaO4OBg4kYcGtVfV1cHQ0ND2rbs7GwiYjIzM8nTYVRUFKysrMh1uHz5Mhl82ZBKpTQhSOHn54fXX38dr7/+OubOnYv29nZkZmay9iVKxNy9e5e1P1y/fl1hP1G0T0363Jw5c/Dqq6/iD3/4Az788EPMmTOHdXKQR5WIefLkCWOlC5/PJ/FDfn5+AGT3m4oVpFIPaMLdu3dp9w/QfHwdChV7oAp7e3sSSGxtbY1169Zh27ZtrNbkF1GGwNPTU+mqnaFcunQJxsbGrOkl5Ff4UGg67xw8eBCBgYHDTllABfO2tbVh5cqV5LjffvstKioqyDJs6iGaQn5cHypiOjs70dPTg7lz5zKOp8m4/qLp6upiGEIoESMWi/Hrr78iJiYGZWVlqKqqwo4dO+Dk5MQaI6ZQxHR2diI9PR1nzpxh/eLg4CACAwMxZswY/Pjjj7Qsh42NjaTTVVdXQ19fn9aQYmNjWZ8UxGKxQn+aUCjEli1b8NZbb5EJi42Ojg5UVVUNe/miVCodVu2KJ0+eoLm5GX19fbQn+q6uLrIioLOzk9apBwcHyWoLdQkLC6PlO9mxYwfNLxgfH09zN3V3d2P69OkK91dQUICNGzfij3/8I+0ptaOjY9j5HmpraxlC6OLFi8Q0P3PmTJUm5nv37inNa3DixAnafZFKpYiMjMT48eNhaWlJEwQUqamp+PTTT/Hee++R1WaALAne/v37AQD6+vq0JZqNjY1wcXHBqFGjWFdhsPHrr79CR0eHEWtAIZFIcPnyZQQHB8PJyQlXrlxhxPmIxWLa/aytrcW6deswc+ZM0o4WLFgAQDboBQQE4KuvvsLu3btp+6mvr4evry9+/vln2moMykrV1taGQ4cO4YMPPqDF87DR0tLCur2zsxPV1dUqA68dHBzIk7Ci/qCsnyhiOH2uo6MDzs7O6OrqQm1tLe7fv4/MzEyygk0RM2bMAI/HI0GsWVlZCAsLIw8N586dg4+PD228S0tLg62tLWNfFRUVsLCwwNtvv01W0QDqZ4IWCARISUlBYmIiLUmipuPrUMzNzVUuj+bxeIzg2MLCQtjb22P+/PmYNGkStLW1MWbMGJiamjKyUo8Ebm5urMkrFZGVlYUdO3awihg2y+7zzDvDrcUWFhZGs5hu3LiRuEdiYmKI1f3SpUuMjNHy7ynx2d/fj7KyMqSkpODjjz9mtG9NxnVNGRwcRGxsLNLT01Wuajp+/DhsbW1pKQeoXFyA7KEyJiYGbm5uCAoKUnqdFYqYe/fuqbVKRCwWKy1Q2N/fz5iknketD7co2++BmpoasmRPnQR68rS0tNDuw9CU2EPF2sDAgMJEgvKfGYnBRlVSpeFYnIZLeXm5UoEklUpRXFxMG5za29tJ21MUk6Ss1hEb8gHtQ6HyrQyXpqYmmmvpypUrtP//8ssvrIF6EolEYdkNiuHUAhoupaWlkEql2LVrF0Pc/tYcO3YMxsbGtEEyNzcXqampSr937dq1YR+rqalJ4X2WSqW4cePGsPepLs8zvhYVFSkN+KSC7FUxODiI9vb2FxYTU1JSMqzcJ93d3SNeO2ik5p28vDza+Cw/d8qP84ODg4yxSNV8rKou0e+duro6dHV1IScnB5cvX1bbqKBWTAzH8+Hr64t33nnnpc+UysGhiIGBAYwdOxaGhoYqU6b/FlRXV2Pt2rX46KOPMGPGDBgYGNCCsTk4OP474ETMb8R/elDn4Pgt+D2289/jOXFwcIwMnIjh4ODg4ODgeCnhRAwHBwcHBwfHSwknYjg4ODg4ODheSjgRw8HBwcHBwfFSwokYDg4ODg4OjpcSpSJmcHCQNZ9Ic3OzynX7EomEdVXAi8joqA6KjtvQ0MDIc/P48eMXlvNAU9iKZg0nOd9wvzPcJE7Py7NnzxjVSV9UW1F1Dbq7u7kVLRwcHBwvASotMRYWFoxkTfHx8bRU6Ww0NDRgzZo1jEx9W7duhbu7u8oTO3/+PK2uR19fHyN1t0QiQVZWFrKzs0mGy6ysLMbf2dnZmD17NiM9PyCrIGpnZ0fbduDAAWzbtk3lOWrCwYMHWTMYenl5sWb9pFizZg1iYmJo27788ktUVFSoTCqXn58PU1NTBAcHw9XVFXZ2dqQEuiJWr15NMgE7OTmhsbGR8RlNREZbWxvc3d0ZImHHjh04evQobZuenh7JzCqftIyiubkZ2dnZsLOzw7Rp09DU1ASxWIz+/n5alsq7d+/Svrd7927cunULYrEYZ8+eZRQr++abb7icPhz/MRoaGuDh4aE0kSIHx3A5efIkamtrAciShWZmZjIyRAcGBiIiIuI/fKbDQ6WIKSgogI2NDc3ykpSUpHKQb25uZi2ctm/fPrXqdQCy+hDJyckAZLVh5syZw5jMHj9+TJsQ5SslqyrcBsjqmvT29tL2oaurS0sTPpJ8/fXXrNsFAgGpmDqUvr4+TJs2DRKJBIODgyQLLVWbyM7ODlFRUbTv3L17F5GRkTh8+DC0tbWRlpaGyspKeHt7w9fXF319fZg1axZrTZeOjg7885//JNf61q1b+OKLLzA4OIiYmBikp6fDw8ODFB5TN436l19+CT6fj4sXL9LSxufk5JA06RkZGaQcwNKlSwHI2tu8efNolprU1FQIBAIcOnQI0dHR4PF4aGxsxMWLF5GUlISvvvoKly9fxvnz58m1o7h9+zatMnRcXByxdBUVFWH69Okala5vaGiAlZUVeX/y5ElcunRJre/GxcXRykf8npBKpbhy5Qq2bt36mx87NjZWZQVjRfwer+nx48eVlk0BZPW/tLS0VJaH0JSqqirs27fvhWZvLi4uhrm5ucIM0sO5N+qe73+ynari93BuRUVFmDp1KgCZtZkty3RAQAD8/f1Zv/977YsKRQyfz0daWhqxaMyZM4cotOTkZJUipqWlBbq6uoyJzNDQUKWI2bt3L+7evYu7d++SiWzz5s3Izs5m/XxSUhJSUlIgEAgwffp0cizq+D4+PnBxcaF9JysrCy4uLjhx4gQEAgFWrlyJnJwcFBUVQU9Pj1hIPD09GZaa52FoMT8KgUCg0EIVHx8PS0tL8Pl8uLm5Yf78+RgcHCTVe9va2pRmSZUvahccHEwsUmlpaaw1ScLCwhAUFARAVreksrKSoc7Zqj2rgq2SalZWFg4fPozm5mbU1dVBKBRiwoQJaG1thZ6eHk6fPo0zZ85gYGCA5uLq7u7G4sWLERISQqo6U53yyZMnpLLvkydPaFVeKagnEkAmEm1sbFBYWIiZM2dqLGCbm5tphfdCQkLUTjmfkpJCq3n0e6K7uxshISF49dVXf/NjJycn4+zZsxp99/d4Tf39/Uk9L7Yq6oCslMDatWtZrZ8jwe3bt/HOO+8MuwSKukilUmRlZUFLSwulpaWsn1F1byQSCbk+6p7vb9VO5c9NXf6TfUgeRfejrq4O/f39jGrd8vxe+6Jagb3Xr1/HihUryPvU1FSVE9jTp08VWmJUFaRavXo1icVpa2vDs2fPSGVkNg4ePEiEEZslJjExkVEFNSMjg/YbVq9ejf7+flhaWtIKI546dQonTpxQer7DYfbs2URk2draIjY2FgKBAB4eHgx3CgBS0fTIkSMQiURwdXWFp6cn+Hw+ZsyYAT6fD4FAAGdnZ1hbW5PvtLa2ktfMmTPJ30FBQTh48CB5n5ycTCswCQDTpk3DoUOHAMiqPctfDwo2ESMQCEgRL0pwxcbGkv+ziZgTJ05g165diIqKQm5uLsrKyrBv3z7U1NRg2bJltM8mJCQAkA0isbGx8PDwgJGREfT19REQEAA/Pz+EhoYSEXPy5ElWEfPkyRNGfanCwkK89tprsLa2ZsTmcABlZWX/8QH4v4nBwUHs3buX9X+FhYU4d+7cCz3+uHHjXpiIoXj99dcVTpqqiI2NRW5uLnmv7vn+Fu106Lmpy++hD3V2dkIsFiM6OpoWenH69GlcvXpVqYj5vaJSxHR3d0NPTw81NTVISUkBIBMx69atU/qUKRKJFFpiFFVOpRrpd999RwsoPnDgAOLj41FaWkp7gqZwdnZWKWKGmsGysrIYIubGjRuMG3jy5EmNTWhDkUgkEAgE5P2ePXtw7949ALKJle3JKyoqCtbW1igtLcXy5cuxevVqdHR0oL+/H6tWrVJYoj49PR15eXkQCoXQ0dGBUCiEUCiEq6srjI2NIRQKcenSJQiFQty8eZMUTBMIBNi8eTMOHjxIrgvbUwebiDl79iyOHz9O3nt5edHUN5uIefz4MU6fPk2rbnrhwgUAwLx58xAUFIS4uDhERETg888/JxXVBwcHERoaCl1dXVhZWZHCfmFhYfD09ISRkRG8vLzg4+NDRExNTQ3Onj0Lc3NzmsXkwoUL2L17N0pKShAWFoZJkyZh8eLF2L17N6MIW1NTE6NNU5abrq4unD9/nny2sLAQhYWFrPdnKOXl5fjll18AyApnBgUFIT09Xe3Cc1euXCHnQxWeFAgEpF+IRCKEhoYiICAA9fX15BoKBALU1NQgPz8fp0+fZhSRu3z5Mvz8/JCcnEwbgNn2p2y7VCpFbm4uYmJicP36dYjFYrViqiorK8l4oWjfg4OD+Pnnn1FTU4MLFy6QayZ/TTU9vrLjanKfbt26hQcPHsDW1hY6OjoQCASMAo7UmDAcbt++jZCQEKSlpanlCtXW1gafz0dBQQECAgJofVwqlSInJwcBAQEoLi4GoPgaK+PNN99ESUkJUlJSEBUVRYvxoe4N235zcnLw7rvvwtPTk8SzKTtfYHjt9NKlS6SvALIijAKBQC3XL9u5SaVSXL58Gb6+vhAIBAyL+G91boqg3P0nTpwg7iz5hStSqZT0e2UiZqT6InVMTfvjUJSKGKlUiu+//x6HDh2Cg4MDvLy8IJVKiSXm1KlTMDExYe007e3tuHnzJmN7XV0dpFIpwsPD8fPPP9P+d+zYMQDA2rVriYhpamrCwoULIZVKIZVKsWnTJjKRUTg5OcHf3x98Ph979+5FaGgo+Hw+UlNTwefz4eLigszMTNp3BAIBQ8TcuXMHDx48QFJSEtnu7u4+YoFOQUFBcHV1JRP22rVrERgYSN5PmzaNds2qqqqQkZGBW7duwcDAAOnp6bC0tAQgm7QcHR3h4eFBBhpFyIsHgUBAyrW7ubmR/QGy++3i4gKBQKCRiElMTGSImMTERNbzoGhsbMTq1avJ+1OnThGLS319PVJSUnDx4kXweDxUVVWRz9XW1mLXrl0IDAxEfHw8Dh06BB8fHzx9+hRpaWkwMjJCY2Mjbty4wbDEnDp1Cnw+H5cuXUJMTAyJC1qwYAEePXoEqVSKmzdvsro94+LiEBkZievXryM8PBxvvfUW7t27h/7+flhYWOBvf/sbAODOnTsYO3Ysw8rFRkFBASZOnAhTU1M0NDTAxMQEEokECQkJOHLkiMrvA7JB4r333oOvry/Ztm/fPhQVFaGpqQnjxo1DQ0MD0tLS8I9//AMDAwOIiYnBq6++ip9++gnOzs5Yvnw5cVECgKOjIzw9PdHV1YXNmzeTAVjR/hRtB2SxZ2FhYRCLxdDT08Nnn30Gb29vpb+puLgYOjo6MDY2Vrrv2NhYvPbaazAzM4O+vj527txJu6aaHl/Zb9XkPt24cQN/+ctfkJCQgNOnT2PKlCkQCoXPvRKytLQUhw4dQn9/PzZs2KCWq0NbWxtWVlZwd3fH0qVLaTFidnZ2uHPnDm7evIk//elP4PF4rNdYFW+++Sa2b98OU1NTfPzxx5g6dSoGBgZo94Ztv3fv3sWHH36I06dPE9ebsvMdbjsVCoV44403EB0dTfZhYWGhllBgOzdzc3PEx8fj6dOn0NfXp8W9/JbnpgqBQEDGZx6PBxsbG/D5fHh4eMDAwACAYhEzkn0R0Lw/sqFUxLi4uGD//v3497//TbNGyE9gFhYWNLN/UlISMjIykJ6ejvT0dAgEAgQEBGDLli20p9f4+Hhi2QFkTx/UzZcXMZs3b8a1a9fI50JCQhjBUdQgIJVKMWfOHNbJJzc3l7afnJwcGBsbk/OZPXs22c+aNWtw584dAMDhw4dpAXZPnjzB5MmTySQ/HEJDQ2n7krfEAHQrEvV7KAIDA6Gvr08E3Jo1a1BXV4fGxkbMnDlTqQtk8uTJOHv2LHg8HvLy8rB+/Xo8e/YM2traqKioIJ8rLi5Ga2urxiLmwoULwxYxzc3NNBebubk5eDweurq68N1335HOIR8P9ejRI2zbtg1FRUU4e/YssrKykJ/7o/uJAAAgAElEQVSfj6ysLAAy362RkREGBgZw/PhxhSJmcHAQlZWViI2NBZ/Px+TJk5GamgoejwcDAwPG0nsAZCXYwMAApk2bRnM1FhcXExEDAOvXr1dLxACyFVOmpqbIysrC5MmTiQXoypUran0fkLUnajAaHByEg4MDAFlFZ3NzcwCyWDUtLS2yMu2jjz4ilq/8/HyMGjUKAPDgwQN8/PHHxEKXn59PBmBF+1O0va+vD6+++irKy8sByOKypkyZotZv2rt3L4yNjZX+BgAYO3YszXUJ/N81fZ7jKzqupvdpyZIluHjxInJzczF//ny1vqOKc+fOYdWqVejt7cX9+/dZ0zEMRVtbm1gNqZgTABAKhVixYgV4PB54PB7mzZtHxiW2a6yMN998k1grRCIR3n77bfKAQt0bRfsdO3YszWWj6Hw1aaeATHhQcXNSqZT8rQ7y53bjxg18+umn5H8NDQ147bXXcPXq1f/IuSlDXsRkZWWRv3Nzc8lYrswSMxJ9EcBz9Uc2WEVMV1cXwsPDUVtbi4yMDBw5cgS7du3C/v37AdBjYvr7+2Fvb8/YR2ZmJmbNmgWJRAKhUIg9e/aQ/0kkEvIDKNauXYuwsDDyt0gkQnx8PDkmIHtqf/r0KYmPefr0KSIiIoglw8rKClu3biXvQ0NDYWxsTKwxbm5uZGL65ZdfWGNiAJnYoAZ2R0dH2k1tamqCtrY27bzU5cyZMypFDNXg5ZFKpTAzMyPmOHd3d1oEub29PZYuXUrERldXF/h8Puzs7GBoaIiJEyeSga2/vx+TJ0+GgYGBQr+7piLm/PnzMDExIYJk586dtN+ryJ001BLD4/HQ19eH5cuXk8l40aJFJDbnyZMnSE1NxYEDB+Ds7IyQkBA4ODjAx8cH9vb2uHjxItatW4fly5fj7t27CkXMUBYsWKAy/xGFk5MT/v3vf9PuV0lJCU3EbNy4UW0Rs3fvXpiamqK7uxvz5s3D22+/DXt7+2Ets62srMQbb7yBR48eQSAQ0CaBxsZGHD9+HP7+/tDS0iIm4PHjxxPT9cOHD8kg6+Pjg3nz5pHvl5eX00zhivbHtl0sFmP06NHEGhoUFESLsVOGg4MDjI2NlR5z6O+goK7p8xxf0XE1vU/ffPPNiIuYlpYWfP755/joo49oDw3KoNwzgKzdUvf21KlT2LFjB0QiEXlR/Z/tGivjzTffpMXEfP3118RiRd0bRftlEzFs56tpO62pqcGoUaNQXV2NnJwcxupOZcif29GjRzFz5kza/z/55BO4ubm9sHNrbW3VaPWkvIjJycmhiRhq4YsyETMSfRHAc/fHobCKmJaWFvI3JWL6+/uJqTo1NZWx2mcoNjY2RHULhUIYGBiQyS0uLg6urq40M6r8MdeuXYusrCzo6+vTPhMZGQkArBPN48ePsWDBAtoS7O+//16hGY7NnUTtV94CsmvXLvKE/7wEBwfDwcGB1Z2UkJDA+L2ATKjt2bMHt2/fRnd3N3x9fRmNbGBgAFu3biXxMT09PVi3bh0yMjIAMMXDnDlzYGFhQd4P7RCaipihMTH+/v5IS0ujHXcoT548waxZs8g1MTMzA4/HQ39/P6ZOnQp3d3fcunWL8b3U1FQ4OjrCyckJgYGBsLOzQ1JSEhISEkhgb0NDA2tgLyViUlJScOPGDeTm5iI3NxdTp05FVlYWcnNzcfHiRWzfvp3V1H/z5k28//77ZKk7RWlpKU3EbNq0SW0Rs2/fPpiamqKnp4csZf/rX/9KE3jqsHLlSjg5OcHJyYm047KyMkyZMgXPnj2DWCymDTra2tpkwJEfZF1cXDBhwgSy3/LycrzyyitK96fsOOnp6Zg/fz6io6NhYmKC6upqtX6Po6MjjI2Nle576O8Yek2f5/iKjqvpfVq+fPmIi5ienh709fXB2dkZr732GsN1zoa8KCgtLSX3PSAgALNnz6aNgVQQPNs1VsZQEbNw4ULSH+TvDdt+lYkY+fPVpJ1SbNmyBTt37oSZmZnaDy9Dz+3EiRP429/+xkjRERAQ8ELOTSKRYPTo0fjyyy/VPl8KSsQ8fPgQYWFh5IHT09OTpPdQJmJGqi8CmvdHNlQG9qanp5McHhTJyckkfoWN7u5uLFmyBD4+PmhpaWFYYlSxatUqbN26leEiUZaA7urVq/D390dUVBQCAwNhbW2N1atXK/Q3swX2sqnbrVu30uJUenp6YGFhoTA4WRlPnjyhPbnLW2IqKiqwZ88eWrB0e3s7UlJSSHDUzZs3yRNfc3MzmUSFQqHSAFJKPEgkEjg6OsLExARffvklsc7IxwABmouYgoIC/PrrryrPQx5FlpiBgQF88803tM9SogyQtUFPT094eXkhKSkJbm5u4PF4AFQvsfb29sby5ctx69YtNDc3o729Hb29vZg/fz7a2trQ29tLXkOD9Lq6uvDPf/6TCGoAxEJ2//59/PWvfyXbhyNibG1tYWJiAj6fT65hcXExbX/qkJ2djQ8++IAW8O3p6UliXR49egQtLS1UVVWhu7sb48aNI666hw8fQktLC1KpFHl5edDS0iIrCe/fvw8tLS0MDAwo3J+Li4vC42zatIl1lZsqqIFT2W8AZE9/Qydv6poC0Pj4io6bnJys0X1atmwZLly4gMuXL2PmzJno6+tjzdcxHOLi4ohby9HRUa1kouPGjSP9qaSkhNz3kpISvPLKK3BxcUF/fz+qq6tx+vRpAOzXWBnyIqazsxPjxo0j5yl/b9j2S61GosYeReerSTul2kxpaSlGjRrF6klQde2oc6OEP5WYdWBgAOPGjUNjY+MLOTepVApnZ2fWsVQVAoEA+vr68PDwwLVr18jDeV1dHR48eABAPRHzvH0R0Lw/sqFSxPB4PNpKDkAW+6AskM3e3h4VFRXo7e2FtbU11q1bh23btrGqXfmI5Lt37+LgwYPQ1dWFoaEh9uzZQ15GRkZ49913VQat3blzB99++y1sbW3h7OyMiRMnYt26dYylfnw+nxETQ03qDQ0NKCwsRF1dHSZMmEBbsVFVVYVXXnlFZcZiefr7+1FYWIibN28iNzcXaWlpiIuLw5IlS3DgwAF4eXnBz88P3t7e+Pjjj7F7927a96ng27a2NqxcuZIIs2+//RYVFRVkGbb8JE/R1dWFuXPnIj8/H87OziTRVlBQEHR0dFBQUAAnJyeauMrKysKBAwcAyJ7s5VfoUCZmT09PeHh4qPztzc3NSEpKQkREBM20StHU1EQTMSdPnkRaWhrEYjHjSdXCwoK0lwsXLjDcScnJyejs7ERDQwO2bNmCnp4eNDY24vvvv6ftJzg4mJaUjmLu3LmscTDyWFtb489//jM8PDzg4eGBbdu2ITo6GhKJBM7Oznj99deJqFFXxFRUVGDWrFmYNGkSQkJCMHnyZJKLSV0RRCGVSmkCFZD1q3feeQfLli1DSEgI3n//fWzatAk8Hg9vvPEGSUp2+PBhaGlpEX/2/v378e677+L777/HTz/9hDfeeANeXl7Iz89n3d/169dZt3d3d2POnDl49dVX8Yc//AEffvgh5syZo9bKLWrgVPQburu7kZmZiVGjRmHDhg3EaiB/TR88eKDx8RUdNzk5edj3qbS0FGPHjiVxBX/+859hbGzMWJ00XKKjo7Fo0SJkZWXB2NhYYYI5Cj6fj7feegtmZmaora3FkSNHoKWlRdzLfn5+eP311/H6669j7ty5aG9vZ73GqjAyMsLXX3+NM2fOYOfOnWSil783oaGhrPvdvn07xo8fj+joaJXnO9x2Kv+wumrVKpUZz4cif26AbDz57LPPEBISgl27dpFQhBd1biEhIYxFMeoQERGB6dOnsz7YU22QzdJPMVJ9EYDG/ZENlSImOTmZoQbj4+MVBrbyeDzk5+fTthUWFsLe3h7z58/HpEmToK2tjTFjxsDU1BQikQidnZ3g8XhklUh3dzdu3rxJlgULhULcuHGDsfwTkFkrcnNz4e3tDXt7e5w8eZI26UokEkRERDBER3p6OknoBsiWcVMTGLUCa/To0fjxxx8ZxywrK1M52ckjkUgwY8YMGBgYwMnJiayeys/PR319Pc3i1NLSQvOxhoWFQV9fn7zfuHEjMb3FxMQgNDQUgGxp3oYNG8jnpFIpIiMjMX78eFhaWtJW9lCkpqbi008/xXvvvUci7QFZEjwq5kdfX59k0AVkvlAXFxeMGjWKdfUZG7/++it0dHTIE5089fX1WLNmDTo6OnD8+HG8//77ZLXVypUrsWrVKqxfvx5r167FokWLiNkyPj4ebm5uqK+vR319PUJCQnDp0iXcunUL06dPh6+vL37++WdMmDABPj4+jOOyTRy6urpqBUWqy8aNG4lLVV36+vogkUhQVVWFZ8+eaXRcedcsRVdXF2mznZ2dai9nfPLkCZqbm9HX10d7gFC0P7btHR0dcHZ2RldXF2pra3H//n1kZmaSVXLKcHBwwL59+57rNzzP8RUddyTuU09Pz4jUB+vr60N/fz/Ky8tZy3NoQmdnJ6qrq5+7hlhHRweqqqpYY/2UIZVKh1UbbrjtFJAFv6uzykqdc2tvb0dpaSnrg/pInltHR4fKPGuKEIvFDBc4hVAoxJYtW/DWW28pzCg9En0ReP7+OBSVIqauro6RK6OhoYHVBNrb28s6gA5lcHAQ7e3tz720sKurC0KhkHF+6qDO0w9b8cvfAvkOn5eXR3tCkRdP8gPM4OAg63UoLy9Xep2lUimKi4tpjbu9vZ00SkW+4uFec0WBj11dXTRxKi+mlNHa2qqw48i3zeEMhDdu3BiRSeXhw4eQSqUwMDBgFY//axw7dgzGxsa0CTY3N5fk9mGjtLQUUqkUu3btIm7C3/L4HP+91NTUkKXFLzrh33D5T58bW/6fkeyLwMj3R7Uy9nJwcKiPjo4Ofvjhh5eukNqLorq6GmvXrsVHH31ELJLyAd9DGRgYwNixY2FoaKi0nMaLOj7Hfze+vr545513fpdFXn9v5zbSfREY+f7IiRgOjhfASHT2/0aGc11exDXk7gsH8PtuB7/Hc3tR5zQS++VEDAcHBwcHB8dLCSdiODg4ODg4OF5KOBHDwcHBwcHB8VLCiRgODg4ODg6OlxJOxHBwcHBwcHC8lAxLxLCtIVdGc3MzamtryfsHDx5oHI08UimKlXH79m2NUoBfvnz5N40oV5SwiA2JRMJ6biORD0UZzc3NpBL475GBgQGlJRKUUVdXR8s/09zcPKzkh8OFLUv1SPQHZZXPAbBWg1cHgUCgdvsSiUQv9NpxcHD8d6O2iJFKpRgzZgxu376t9s5v3rxJyrgDwPr160m6ZKlUyioY6urqcPDgQVrCt4GBAfz9739nTUucl5cHb29viMViVFVV4erVq4xXdnY21q5dqzLxmaWlJZKTk9X+fRSnTp3C5s2blX7m6tWruHPnDu7du4eoqCg0NTWRc6NKHwgEAmzZskVlIrlPPvlE7RTNDQ0NWLNmDSmwSL22bt3KWmOlqakJ1dXVjJefnx9Zy9/W1oavv/6aVn4dYC6X27ZtG6PuFhsnT54kYre8vByZmZm0ayIQCBAYGDiieVe6u7vxt7/9bVhVoinCw8NppTgSExNJtsnOzk5aJuORaJ+enp60dtnR0YHPP/+ckVhycHAQFy5cwMWLF2n3OiMjA3FxcbRtTk5OWL16tdIMr87OznB0dBzWtbl//z7+8Ic/qJ24KjIyknYM+UJygOx6ypcMEYvF2LRpE6M9D33t3r2bZLPm4OD470VtEXPt2jVaWnuKsrIyImz6+voQGRmJjIwM8Pl8+Pn5YeXKlWRgWbhwIc6cOQM+n4+AgABSn2codnZ2tDIBcXFxSidDc3Nz+Pn5obW1FY2Njejs7MSyZctQX1+Pzs5O8lKVIXjXrl2k9IEyMjMzaUUCGxoa8MMPP9C21dfX06pfe3t7Izo6GiKRCLNnz8bDhw/x7NkzjSwiX3zxhdqfbW5uxvLlyxnb9+3bx/qkXV5ejpKSElJvqbS0FFOnTkVpaSnJPmthYYFTp07RvicWi2Fubk6bSBITE+Ho6Ejb5ufnR0oaUBQVFWHq1KkAZOKCTdwGBATA399f7d+tio6ODtZaThTyWamHtms7OzvY2tqS33To0CFYWFiAz+fD19cXu3btopUveN722dPTgzFjxhCLxenTpxVaSdhE2cWLF9UqGJeenk5rwxUVFbCxsaFtKy8vV1gZHgCp4XPixAm0traqPOa5c+cQHh5O3u/YsQORkZFEvNrb2zNKf6hTwff48eMKa8BwcHD898AqYrq7uxEREUEGbT6fDz09PQQHBzOeeEJCQhAcHMzYh1QqRUFBAX766SeyzcjICOXl5SpPqry8HLq6uuS9tbU1scywVZqmJgd55AdtqlK0PJWVlSS1M/X67rvv4OXlRdtmaGhIszjU1NRg9erVyM3NVfrasGEDVq1aRdL2+/n5gcfjoby8/LmzMU6aNEntz7a0tEBXV5dh2TA0NFTqLliyZAmZxOWvZVJSEuLj4xmfj4iIIKL0/v37tPTUQqGQWGl2796NrKwsRi2VoQU6Kerq6tDf36+0uqomiEQizJw5k3FdqNeRI0dw5swZ1u/GxcUhOjoaly9fRkNDA3g8Hq1q9FA0aZ+UpZJ6CQQCdHZ2orW1Ff7+/mR7fHy8ylINM2bMoAlqNoqKiqCvr6+yXRsaGsLIyIi1bEdQUBD27t0LQGZBWbduncqCrfHx8QgPD4dEIkFFRQXMzc1pbSEuLo4hmGfPno3e3l5SjJR6NTQ0wNraGs3NzZyI4eD4H0EtS0xhYSEpo93Y2Ig1a9aorD3k7u6OAwcO0Hz3xcXFtO/JV+js7e1FVFQUEU6JiYng8/kICgpCUFAQ+Hw+fHx88O9//5vxhEdNiH19fYiKikJCQgLmzJlDJqRPPvmEUXn1wYMHMDIyom3bs2cPwxIza9Ysmpvk+++/x7FjxwDI3EjUU6mXlxe8vb0ByAbwoYX//P39wePxcObMGVYhJv9bYmJiFP4fYIqYhoYGhZVYnz59qtASo6yQGJuIKSoqwpUrVwAAjx8/pl2XCxcuoLq6Gl1dXfjyyy9JQcaioiLcv38f3333HbFGsEFZIqKjo5GVlUXuHWV1GGkRU1JSAkNDQ42+SxUtdXJyQnh4ODo6OiAWixW6ZjRpn5Rr6PLly0hMTMTixYuJ++nAgQOwsbEh7xUJQADIyMjAq6++yhACQzExMVF6fSMjI9HT08MqYAFZXNj27dtp4rS2thZz585lTSmekJAAPp8PU1NT2NraIiEhAV5eXti5c6dKETNnzhx0dXUhOzsb9+7dY7w4EcPB8b+DShEzMDCA1atXkyeqlpYW1klRnp6eHkybNg3V1dWIiIhgWG/s7Ozg7e2NQ4cO0Qb+4VY6rampwdmzZ2FgYIDMzEyy/erVq7CzsyPv2c734cOHaomYoWZ4eXN9S0sLPvjgA3R2dsLBwYGUZmeDEjFUmfakpCTweDzw+XwcPXoUu3fvJvELiYmJSp9gJ02aRLueDg4O2L17N6uwFIlECi0xOTk5jM+3trbiyZMn+Prrr1FTU4MnT55AV1cX169fR0hICABZm5g9ezYRbfLXw8zMDCUlJejq6sKECRNItfP29nbMmDGD5hZsa2tDZGQkTpw4ga1btwIAzaUilUpJcciRFjF5eXnYvn27wv+zuXb6+/uxYcMGct1/+OEHHD16FHw+HzweDxMmTKDFtTxP+5SnuroamzZtIu+jo6MREBCg8je2tLRg4sSJsLKyQnBwMCorK5GUlKTw8z09PYiMjGTEaWVmZiI8PBwPHjxg/V5mZibWrFmDyspKRizVsWPHMHr0aCxevBjx8fGMYOKlS5fSqptbWlrizJkz5NiOjo6sIgYAHj16ROuP8sVqORHDwfG/gVIRI5FIsHjxYly/fp1sa21tpQ26bEGo+/fvJ64FKjZAKpWSVTVsYuF5MDU1RUVFBfHb+/n5ISwsjLxftmwZOjs7YW9vT+IKysvLNRIxgMxqcODAAdTV1ZF4oG3btil1zwQEBIDH46GxsZGIgZaWFgwMDIDH46k1KVEMx53U3t5OCzSlqKurg1QqRXh4OAm2BmRWt9u3b2PKlCm4ffs2bt++jUmTJuH27dvIz8+HRCKBsbExgoKCaPsbGBjA+fPniSjdsmULIiIicPPmTaSkpACQPZlHRkYyzkUgEOD48eMAAB6PBxsbG1LJ1cDAAMDIi5ijR4/CwMBAoTvps88+Q2VlJeN7S5cuJX/b2toiLy+PvJcPYpdHk/YpjyYiRiqVwtDQEKmpqfDy8iJtjopte/r0qfILpIDr168TC5xEIkFoaCjCw8NRWFiIBw8eoLy8nAiP8vJylJeXQyQS4fTp09DX16dd08bGRowaNQpWVlbYvn07+vv7YWlpqZYlBpAFVJuZmRFRaWtrS1y1nIjh4PjfQKmIiYiIgI6ODtra2ohF5fz585gxYwYZODZu3Eib+PPy8rBgwQJs2bIFALBgwQIIBAIEBwdj4cKFADQTMRKJBIcOHUJbWxvjf6ampigoKEBCQgLr6g/5F/VkX1ZWhoULF9IsGmvXrmXExCgKor106RL+8Y9/kPdLly6FSCRSeP6UJQYAdu7cifb2dri4uOCnn34iIiYlJUXpahEKdURMUlISMjIykJ6ejvT0dAgEAgQEBGDLli20yTo+Pp6IDHn09PTI37NnzwYgc4fs2bNHaWAnADg5OcHLy4u85/P52L9/P+u9A+giJisri/ydm5tLJqWRFjGPHj1irK6S5/PPP2fd/s0335C/jYyMaG48SsQMDdYebvscHBzEzZs3IRQKIRQKceHCBSxZsoS8P3ToEPbu3QuhUIiff/4Z+vr6jGtra2tLVnPJixhAdo21tbVRVlZGtv3yyy9kVdjFixdx9OhRCAQChIeHIzAwkGw3MDBARkYGANlKNirmSx4vLy8kJiYqvLYUjo6OsLa2Rnh4OA4ePIiqqipYWVmpLWKSk5Np/4uOjibWUE7EcHD8b6BQxJw9exbp6ekMS8RQS4w8VVVVCA8PR3Z2NnEjUMLl3r172LdvHwDFIqanp4cRUEy9UlNTERISwpp7xNTUFNXV1QBk7hNPT08kJiaSV0BAAGOpaFlZGc2MDciCToeel3xgMiCzJlCmck9PT/L3xIkTyd8VFRVYvXo1zp49S77n7+9PxIKZmRmKioowd+5cPH78mIiYzs5O7NixQ6VbbTiWmMzMTMyaNQsSiQRCoRB79uwh/5NIJKyB1q2trVi/fj15P2vWLFRVVSEsLIzEtAwMDGDv3r20c+3q6oKlpSW2bt2K1tZW2uvw4cOYNm0avv32W7i6utImUHkRk5OTQxMxLi4uANhFTGtrq9L4ouGSlZWF2tpadHd309r9s2fPwOfzIRAIMHv2bCIAv/jiC6SmppL3urq6cHd3h56eHs3NoUn7rKiowLNnz9Da2ors7GxYWVmRaxkUFISTJ0/Srq+8m8bDw4O2xHmoiAGAgwcPKlydJRQKYW5uDoBdRAxFLBbTXEgHDhyAv78/qqur8fDhQ+zdu5dhYWpoaMDOnTtJYC8FmzvJ19eX9l3q3qSkpMDHxwdisRhisRiRkZGciOHg+B+DVcTU1taSp9ThiBhqIJUXMYsWLQIgEzE2NjYAXow7qbq6Gnfu3EFBQQHDTTR08lYEm4gZyu3bt3Hv3j2UlpaSV0JCAhYtWkTbVlpaSntK9/Pzg52dHebNm4cVK1YgOTkZsbGxAEBzJ7m4uODo0aNKz2E4IsbGxoYEGQuFQpoLJS4uDq6uroz4j6SkJJoA09XVZQirbdu2YcWKFWhubsbg4CDS0tLg6+uLpqYm6OnpEVcU9Zo6dSqKiopw8eJFnDx5khYUTImYhw8fIiwsDCYmJhAIBPD09MThw4cBMEWMRCLB6NGj1VpuK/+dmJgYWuCw/Cs0NBRZWVlkpQ6FfGwORUlJCVavXo28vDyaIGPjedunj48Pzp8/T95HRUWxupP6+vrg7+/PiF1hEzH9/f04ceIE6/ECAgKIu5BNxLDls7l8+TJKSkpQWloKe3t7+Pj40PqC/JJzQObO6+jowLlz52irwNjcST4+PrTvUmPS+fPnYW5uTu6fnZ0dyQ3DJmK8vb3h7u7OupqSg4Pj5URlYO9wRAxFVlYWiYmZN28e+Hw+AgMDybaRFjEmJiY4evQoLly4AJFIxDpJqJN0bdeuXbQYB3WQSqVYs2YNXF1dceTIEVr8kDynTp0Cj8fDo0eP0N/fj0OHDpH/yYuY+vp6bNy4Uekx1RUx3d3dWLJkCXx8fNDS0qK2mLOysqJZOKZNm0b+HhwchLm5OZydnYkQefbsGc3yIO9yoZg/f77CPCgCgQD6+vrw8PDAtWvXyHLguro6MiEPFTFSqRTOzs5q5T8ZLjExMcRqyAYV7E6JF0dHR9y/f1/h55+nfYrFYixZsoTmtomMjGRYEQGZMGGz4p04cYIhYpSxbNky1NXVAWAXMYpWKFGocidVVlYS12tsbCwtKZ2FhQVDxAwNIKfcmwUFBbQs4o8fPybtdqiI6e/vh5ubGwCZpYqDg+O/g+cWMU+ePKGZgwGQFTOAzAIByJ6CqVgBa2trjUXM3bt3YWVlRRusDQwMSAbVhoYGGBgYoL6+nrxSU1NpGVYVYWVlNaxU6z09Pfjxxx+JAOnq6oKNjQ0mT56MX375hfZZb29vEhOTnJyM9vZ21NfXo7a2FlFRUbRJRlXpAzYRk52dDVNTU9p1sbe3JwGl1tbWWLduHbZt28Yax0Ddm+LiYtpT/+DgIHR0dADI7vWOHTuQnp6u9Pzkg18p5s+frzDNfUREBKZPn84qcqgVV76+vown65CQEFpQ8vNQU1ODkpISPH36FHp6euDz+ayfa29vx44dO5Cbm0u29fX1Yf369XB0dCSTvzzP0z4dHBwYvzE8PJzhYlHG8fN/AjgAACAASURBVOPHGYHYiggNDcWRI0fI+3PnzjEsIba2tkr3wSZiysvLWRcBREZG0nLsmJmZMdxJJ0+epH1HmfWtpqYGTU1N2L59Oy1XESBzoQUGBj53niYODo7fDypFzIwZM8Dj8UhwaFZWFsLCwsggQw1y8hNvWloa60BXUVEBCwsLvP3227RVCtRSW0UrReRfKSkpSExMpAXRNjY2ksm7uroa+vr6tLwRsbGxallizM3NWZcds/0Of39/ODo6si475fP5+Mtf/kITFJ6enoxBtb+/HwcOHMDo0aNZ87w8ffqUdYm6she16oTH4yE/P5+2v8LCQtjb22P+/PmYNGkStLW1MWbMGJiamkIkEqG7u5uxBFcsFsPV1RWJiYkIDg5Wq7bUjBkzGPdNR0dHYYp/sVissB6UUCjEli1b8NZbb6GgoIBs7+joUJrnZrh0dnbC3d0dr7zyClauXMn6//T0dJw5c4Y1QHlwcBCBgYEYM2YMfvzxR9p10qR9Njc3w9XVFbdu3WIcKzQ0VKEriA0XFxelyfgAmbg6duwYzY0IAL/++ismTpwICwsLWFlZwcjICH//+99JCoB79+4hPj6e1gZPnTqF0NBQ2rbDhw8zLCoAEBYWRnON7dixgxanFR8fT3M3dXd3Y/r06Qp/R0FBATZu3Ig//vGPrPWl1MkizMHB8fKgUsRcu3Zt2DttampSONlJpVLcuHFj2PtUl/7+fka+FHVT+xcVFSkNFJVKpbh27RoKCgpUljAYKkrKy8tZl89KpVKVNZ2GS29vL6OuDhuDg4Nob28nv4XNQgPIglGHUx6BLfhambtFHYZbfFRT8vPzWV0y9+7dU6vIp1gsVlrQUN32KZ/leCgVFRWMGB1lPH36VOm5P378GNeuXVP4md7eXjQ1NUEkEjGSHD4vLS0ttP0NHTeG3ouBgQFGYsChDAwMKF0pyMHB8d/DsKpYc3BwcHBwcHD8XuBEDAcHBwcHB8dLCSdiODg4ODg4OF5KOBHDwcHBwcHB8VLCiRgODg4ODg6OlxJOxHBwcHBwcHC8lHAihoODg4ODg+OlhBMxHBwcHBwcHC8lnIjh4ODg4ODgeCnhRAwHBwcHBwfHSwknYjg4ODg4ODheSjgRw8HBwcHBwfFSwokYDg4ODg4OjpcSTsRwcHBwcHBwvJRwIoaDg4ODg4PjpYQTMRwcHBwcHBwvJZyI4eDg4ODg4Hgp4UQMBwcHx/9n77yjorj6Pj5Y0Gh68ryaannUJMaaYu8Vu49YCDEoRrAQsXfjq8bYEEEEEZAuoiA2yoorNgiIDQSlCAKCsCAgZens7vf9Y8/cZ4eZXXYXNZL3fs6Zc3Zmp932u9/7u3fupVAozRIqYigUCoVCoTRLqIihUCgUCoXSLKEihkKhUCgUSrOEihgKhUKhUCjNEipiKBQKhUKhNEuoiKFQKBQKhdIsoSKGQqFQKBRKs4SKGAqFQqFQKM0SKmIoFAqFQqE0S6iIoVAoFAqF0iyhIoZCoVAoFEqzhIoYCoVCoVAozRIqYigUCoVCoTRLtBIxsbGxsLGxQXFxsdY3lslkqK+v1/vFKBQKhUKhUDShlYixtbUFwzAwNDTEyZMnyfH6+nqUl5ejurqac/69e/fQvXt3zJgxAwqFgvPfhQsXYGRkhIcPH+r8skeOHMHWrVt1vo5CoVAoFMo/D627kyZOnAgDAwN88cUXAIBbt27h888/h5eXFxiGwalTp3D06FF06dIFDMOQbdasWZz7hISEkP+2b98OmUwGU1NTzjWatrfeegubNm16ubFAoVAoFAql2SEoYrKysvDxxx9rLSwabv/+978xa9YsmJiYwNzcHI8ePeLc/8CBA2jTpg3atWuHW7duobq6GhMmTMDkyZPx22+/wdramrctXLgQLVu2ROvWrWFjY/NaIodCoVAoFMqbi1pPzMOHD/Hpp5+CYRgMHToUa9asgaWlJT744AMiVlauXAkzMzOyn5aWBrlc3uhDq6qq8Pbbb6Nt27aIi4vTeG5JSQkAICcnB61bt0br1q11DCKFQqFQKJR/Ihq7kyoqKohA8fLyAgDMmzePHPPx8UF6ejratm2rs7gQi8Xw9/dHWloakpOTBbfY2Fh07NgR69atoyKGQqFQKBQKh0bHxLi4uMDQ0BBt2rThiJrc3Fw8f/4cT548QZs2bQTFRUpKitr73rx5ExUVFfDz89Oqi+qXX36hIoZCoVAoFAqBJ2LkcjkkEgnZ0tLSiJBgBYe5uTmSk5Px6aefYvLkyTA0NETr1q1RX19PtmvXrqFVq1bYtWsXamtrsWfPHixcuBArV67ETz/9BIZh8N1330EqlSI4OBjHjh2DSCSCWCwmW1hYGPnt6ekJAwMDKmIoFAqFQqEAUOOJWbVqld6DelU3AwMDMAwDR0dHXLt2jfe/oaEhIiIiAAADBw7EpUuXkJOTg5ycHKSlpaFXr144dOgQADomhkKhUCgUChe13Ul2dnawt7fHuXPncO7cOaxZswYtWrRAjx49yLFz587Bzs6OiBI3NzcMGTIEBgYG6Nu3L9zc3ODm5gZ3d3dUV1ejoKAAdXV1cHNzg4GBAcaNG/ffF9EghNzd3amIoVAoFAqFwkHreWL8/f3RokULDBw4EAAgEokwceJExMXFkc+lAcDBwQEGBgaYNm2a2nsJiZi33noLb731FhwcHBAcHIyVK1fCwMAAnTt3RllZGRUxFAqFQqFQOAiKmOfPn2PatGladRe9//77aNWqFd59910AwOHDh/USMW+//TbatGlDPrlmx8CMHDkSAO1OolAoFAqFwkWtJyYiIkKjgPnqq68wbtw4GBoakgnuAP1FzHvvvQdDQ0OOiGnRogWGDRsGgIoYCoVCoVAoXBrtTurWrZugiLGyskJNTQ0cHR05gsTe3l5nEXPgwAG1YmnQoEEAqIihUCgUCoXCRaOIefDgAVq0aIG2bdti5syZaNGiBb755hu0bt0aLVq0gJGREaZPnw6GYchSAHZ2dlqLmDFjxnBm/GUYBu3btyczBTMMg379+gGgIoZCoVAoFAoXjSKGFSirVq3iDOw9efIkWrZsiXfeeQdt2rQBwzBISkoCoFzxWlsR06JFCzAMgwkTJoBhGLRq1YrTncQwDObNmweAihgKhUKhUChc1IqY27dvg2EYfPHFF6itreWIGIVCgZEjR+Kzzz4DwzCcsS1s15AmEXPo0CEyh8wnn3yCyspKdOrUCU5OTqipqQGgXLtJdeFIKmIoFAqFQqGoIihiqqqqSHdO//79MWbMGEybNg0tWrTAkCFDAAAJCQnknKdPn5Jr9+3bB4ZhMGnSJLUPnT59OgwMDNC2bVvEx8erPS8gIADLly+Hvb09Dh48qHZ5AwqFQqFQKP//EBQxYWFhYBgG9+/fR//+/TmfVJuZmeHBgwfk2O3btznX7t69GwzDYNSoUWofmpubC4ZhsH37do0vd+jQIc7svgzD4KOPPtIjmBQKhUKhUP5pqO1Oqq2tJb/Ly8vRvn17tGjRAo6OjkRYJCQk8K7bvHkzGIbB8OHDNT44Pj4eMpms0Resrq7G0aNH0bJlS7Rp0wZ2dnaNXkOhUCgUCuWfj9Yz9t68eRPFxcVISEjA0qVLUVFRIXjesWPH8Oeff6K8vPylvSQAuLu7k3WWKBQKhUKhULQWMRQKhUKhUChvElTEUCgUCoVCaZZQEUOhUCgUCqVZQkUMhUKhUCiUZgkVMRQKhUKhUJolVMRQKBQKhUJpllARQ6FQKBQKpVlCRQyFQqFQKJRmCRUxFAqFQqFQmiVUxFAoFAqFQmmWUBFDoVAoFAqlWUJFDIVCoVAolGYJFTEUCoVCoVCaJVTEUCgUCoVCaZZQEUOhUCgUCqVZQkUMhUKhUCiUZgkVMRQKhUKhUJolVMRQKBQKhUJpllARQ6FQKBQKpVlCRQyFQqFQKJRmCRUxFAqFQqFQmiVUxFAoFAqFQmmWUBFDoVAoFAqlWUJFDIVCoVAolGYJFTEUCoVCoVCaJVTEUCgUCoVCaZZQEUOhUCgUCqVZQkUMhUKhUCiUZgkVMRQKhUKhUJolVMRQKBQKhUJpllARQ6FQKBQKpVnyxooYuVyOqqoq3vH8/HxUVFRovPbWrVtQKBRaPSc3N5f3nOfPn6O+vl77l9UDqVQKsVis0zWxsbFNep5cLif7CoUCEolE7/up49GjR3rF3d+VDm8C5eXlL+1eMpmsSdc/fvxY67KjC5WVlYLlWZXCwsJGz1EXvry8PNTV1Wn1LtnZ2Zw4LygoaPS5fxdPnjwBAEgkElRWVup07Z07dyCVSrU69+8sf7GxsVq/JwA8ffqUsy+TyRAfH/+yX0srKioqOHZViNTU1EbPUSU7O1vn90hMTNT5mn8Kb5SIKS0thUKhQEREBIYOHYq1a9ciJycHcrkckZGRWLlyJXr37o1t27apvYdMJsNHH32Ev/76i/efkHHev38/Nm7cyDm2bds2/Prrr4L3/+uvv2Bvb4/6+npkZGQgMjKSt12+fBmzZs1qtHJaunQpDh06pPEcVfr27UuMeEZGBoqKinD9+nVkZmYiMzMTTk5OqK6uJudXV1dj+fLlePHiBYqLi2FiYoKamhoAQGRkJP7973+jrKxM6+ezVFRU8AwJy4MHDzBmzBgA/624SkpKAADR0dGYMmUKjhw5wrtO13QAgEOHDpH3SEtLw6VLlyAWiznbsWPH4OXlRa6Ry+VYvXo1MjMzASjFnVDFWF1djaysLA2xoBtz5szBxYsXIRKJOFtgYCC+/PJLXl4pLi7G0qVL8fz5cwBATU0NamtrASiNnJ+fH+bNm4cZM2Zw8nV2djZWrFiBuro65OfnC+ZPd3d3/P7774Lv+fPPP+P8+fNah+vhw4e8+0dEROA///kPiouLyXmPHj3C1KlTedcXFBSQyjk0NBTTp0/X+LyysjKYm5vj2bNnnOMnTpzAli1byL4mMefp6ckJf2BgIFatWgVAmR9UGwtNKe/65M+GzJ07F/Hx8bh48SKsra01xk1DVqxYoXVa6lP+XgYymQxdunTB4cOHtb7m0KFDOH78ONkPCQnBqFGj1Aqul5EOgLL+cHR0JDYUACIiIrB06VKN1/3yyy8ICQnhHFNnd+VyObp06YJr165pvGdDevbsidTUVN7xjIwM2NjYoFevXkhPT9fpns2FN0rE/PDDD/Dy8oJYLCYVn4mJCRwdHREfH8/JPIAyI8TFxXG2Y8eOYcKECbzjDg4OGDFiBM/QrFy5EtXV1ZyKYMCAAaQFJISVlRUcHR1RUlKCvLw8SKVSGBkZIScnB1KplGxsoVIoFKiuruZtSUlJCA8P5xxLTEzExo0byftIpVIShkGDBuHQoUM4deoUunXrhpSUFMyYMQPJycm4fv06evbsifz8fM67Hj9+HMOHDwegLEx79+4FACxfvhwJCQm8sMnlcvj5+SE8PJxX0Nlt9+7dmDJlCue6iIgIBAcHQywW49ChQ3B1dYWNjQ18fHwwduxYnDt3DuHh4cjKykJpaSnvufqkw4MHD9C/f38ASsEk1JpzdnbG0aNHOcdu376NsWPHAlBWUubm5rwwent7w8HBQe2zdWXMmDF48OAB0tLSONuDBw8wYcIEwWu8vb1JpZ6fnw8jIyP4+fnB29sb169fJwK/IZs2bUJ4eDiqqqpQUFCAtLQ0DB06lJM31bV8Fy5ciOTkZK3DlZ+fj8LCQpSWlqJfv364fv06J//fuHEDcrkcGRkZWL58Oe/6ffv2YcGCBQCA8PBwrFmzptFnnjlzBlZWVqivr0dgYCBEIhE2btyI1atXQyQSYffu3Vi/fj05v6amBt7e3ggLCyPnrlu3jgjJHTt24LfffoNIJIKDgwNWrVrFqWR0Le8s+uZP1TJgamqKwsJCiEQiIv5Z29gYq1atQkxMjFbn6lP+XgZubm4wNzeHm5sbR/QKwb5bdXU1+vXrR46vXr0aVVVVaj2I+qSDSCTChQsXEBAQgJ49e0IkEiE4OBienp5ISUkh5928eZPYVAB49uwZfH19OQ0VIyMjHDt2jOwHBQVh9OjRSEtL473HqVOnMHfuXBQWFvI2kUikNj0GDRoEAKirq8OVK1dw5swZbNiwAaNGjUJYWBgePXr0Uj2+CQkJsLKy0str1BhSqRQuLi5wcnLS6vw3SsSwGU2VX375BXfu3BE8v66uDgkJCSSRJRIJ+vfvj4cPHwpmgsLCQiKEwsPDsWfPHtja2kIsFmPq1KmIiIjAgwcPMGrUKJLhDh48yGuhsMZMlSFDhpDfDV17crkcAQEBiIiIwLVr17TaWCMqk8lIGEePHo3nz59j4MCBiI2NxbNnz2BmZgYAcHFxwZkzZzjPffr0KcrLyzFv3jwAypbu7du3cf/+fZw9e5acx3olGqKuNXD+/Hls2rSJd/z48eOki8rY2Bi3bt0CAIwbN07wPkDT0gGA2go3OzsbtbW1cHZ2xrlz5wAoK1y2hZ6UlISoqCjcuHGD04J/VYwbN06tiFEXPzU1Nbhy5QoAZZfTrFmztHpWaWkp8doASsO4e/duzjlXr14lglcmk+Hu3bu4c+cOpk2bhjNnzuDOnTuwt7dHVVUV1q5dS8Sdn58fdu3axXumg4MDjhw5gtzcXOTm5pLjQ4cORVZWFjIzMwVFzODBg/Hw4UNIpVJcvHgRK1asIKIgKipK0GunUCh4Yt3f3x+enp4AlBVjYGAg7xzVc319fXHjxg3k5uYiODgYx44dUxObupd3VXTJnyzPnz/HrFmzIBKJMHr0aJw+fRq7d+/GsmXLIBKJ8M033/A8oU+ePMGBAwc4led//vMf2NnZcY7NmzePkz5NLX9NISsrC9988w1KS0tRXV0NCwsLjV1mISEh2LRpE0QiEc6fP4+DBw/CyckJ3t7eEIlEmDZtmtq00CcdAKXNtLS05B03NjZGVVUVIiMjOSJGCEtLS857qfOI1NbWYty4cbhz5w6vER4XF4eff/4Zp0+fJucXFRXB19cXYrEYPXv2REhICExMTMhwi7i4OMEy11QUCgXCw8PBMIxODR5tycrKgpGRkWC8C/FGiZh+/fqRQjNr1izcunVLUMSoc/Pb2tryjF5tbS1u3LjBOzcsLIyT+aZPn47a2lqsWLGCY/yOHDkCW1tbzrVs67empgY+Pj4ICAjAkCFDiKHv3r07z93Nkpubi5ycHN525coVrF+/XuN4hAkTJsDBwQE2NjakEmRFzNq1aznXPnv2DJaWlrzuC5FIBDc3N/J737596NChAwoLC3nPu3DhAqZOncrznJw+fRo7duzgnZ+YmIj58+ejurqa0+U3ceJEwfgDmpYOAEgL2NfXl+M9cnJyQmRkJMc4vXjxAjNnziSi7dy5c7hz585rETEjR44knirVLTg4GKNGjQLA7+6sq6tDQUEBAKCqqkpQxNTV1eHixYsAlK0jDw8PAMoycvbsWYjFYgwaNAgnT56Eq6srTp48CbFYDB8fH1y9epXcJzk5GcXFxfjpp58QFxeHkpISDB8+HOXl5Rg9ejQ5Ly4uDuvWreO8w/Xr13Hq1Cmyv2rVKkRERAD4b9oLiZj4+HiYmJiQbhk7OzvMmzeP01UTFRVF8sv9+/fh7OwMQDlGJDY2lnQNbNmyBevWrYNYLMbq1auxbds2WFtbCwoZiUQCqVSK33//HZ6enigvL0d9fT3P08vSlPKuS/5kKSkpIWkt5IkR8tylpKQQW8CyZs0anidm0KBBnHzW1PKnL5WVlRg/fjwePHiAu3fvoqioCMnJyTAyMuIJRpbQ0FBs376d7DcM39y5c9V6GPVJBwD49ddf4ejoiJiYGNjY2BDvNdtlLiRinjx5An9/f/KMKVOmwM3NjexbW1sLjm/csWMHr+FYXFxM0mvFihW4f/++YPhURTVLQxGj7lp9adWq1SsRMYDShjRLEfPDDz+Q32zhnTt3LpydnSEWi7F161Z4enrit99+41W6UVFR+PTTT3H58mVOJeHn54fu3bvzMnd4eDiv8N66dYuXkQ8dOgQ3NzcAyorh5MmTmD17Ni5dukTOiYyM5LRSJk+erDaMt2/fxoMHD5CcnCy4qRrShm7w5cuXw8jICGFhYfDw8EBQUBDMzMxQVlaGwMBAznMsLCw0ti7z8/OhUCiwefNmTsusIfPnz8eyZcs4x3x8fDhxV1lZCR8fH4hEIjg7O2Pr1q3w8fGBp6cnrK2tMXHiRIjFYoSFhcHf3x8jRowglbM+6VBaWgpvb2/Y2trC3NwcADiufIVCQcaSNDROp06dwtatW1FcXIyQkJBXKmJqampIZXzkyBHB8RTXr19HUFAQNm3ahJkzZ6Kmpgapqak4ffo0du7cSURAeXk5RowYQcTn5s2bYWdnh6CgIDg4OJCxUD///DPCw8NJH3xKSgrxxAFKsatpEKWJiQnxpo0fPx4A15MWFxeHDRs2kP3Q0FB06tQJ69atg4WFBdzd3XHq1Cl06NCBdIEBwiJm5cqVKCoqIvtisbjR7qQlS5bAzc0NFy5c4Bxv6IkJCAjgXVtbWwsTExMSh/Pnz8fu3btJV8HXX3/NcbnrW96bkj8BZYWrq4h5/PixViKmYWWnT/nThZqaGohEIs6xsrIy/PTTT4iOjgYAHDhwgDR6YmJi0KVLF+zbt4+TNwDg8uXLmD9/PrHts2fPhoODA9kfOXIkJ/2amg5lZWWYMmUK8QyOGjUKUVFRAP5bJvTxxABKD5Bq4zwqKgpubm4YM2YMp/765ZdfcOLECQDKvP/o0SPBZ7DpKhaLcfbsWYhEIjg5OWHq1KnEc7Vu3Tq1tj4yMhJOTk7Eew4oGyfsewDKhpRYLMb169cBAIaGhkhKSsKFCxfg4+PD+eCGHdvq7OxMhJ9cLseVK1dIA+vBgwcAlHnXxcUFoaGh5NiaNWuIiJFIJDh+/DicnZ2Rk5PDe/c3SsSodiexhXf8+PHEdbpo0SJB5RcdHY21a9fixx9/5P0nkUgEW7BisZhXeO/evYuUlBQEBQWR4/v37+cN+LK0tER6ejoZx+Lo6Ah3d3eyb2RkBKlUik2bNmn91UN1dTUOHz4s6Impq6vDo0ePMHfuXFRUVCAjIwPl5eV4/vw5zMzMIJPJsGvXLo6HY8+ePXj27Bk8PT15nhhXV1e88847CAsL4wwEFiI1NRU+Pj6cY8ePH9c4ILmhUWcrsrCwMNjZ2XH+a0o6iMVi2NjYAACCg4Oxdu1aiEQiHDhwALNnzwbAN04KhQKVlZVwc3NDVVXVK/fEPH36lLR22fwxbtw43vio/Px8VFdXc9JQNXzPnz/ntEwcHBx4lQOgFC03b95ETEwM6uvrMXPmTEgkEpw4cYIMTh01apTarzmMjIxIVxTb2mTFDMAXMey7A8CsWbNIpenn54eqqiqSFxqKmNu3bxNvjWp4G4qYhmN+8vLycO/ePXh6eiI3Nxfnzp3jeWJ+//13jpdJFVWv4Lp16zgfAKgbm6RvedcnfwJKETNs2DCIxWKMGTMGQUFB2Lt3L6ysrCAWizF06FDeO6p6ZVm0ETH6lL/Kykp4eXmRxpWmzdPTE5s3bybjeO7evYstW7YQ8QDwBVJWVhYmTJiAIUOGcMaAhIeHN+qJERpvp2867NixA1u2bEF9fT0qKio49RObV1RFjFC8nD59Gp9//jmOHDlCjgUHB+Pw4cPYu3cv6uvrkZ2dTRqh7Fg9lu3btxOxs2DBArVdUYMGDUJQUBDOnj1LhJy23UnW1tYIDQ1FVlYW3nvvPdJlFRMTg9atW8PX15ec+9tvv3FEzOLFi2FpaYlOnTqhf//+5AvBjRs34u7du4iNjcXHH3+M4OBg+Pn5oWXLlli2bBmMjY2xdOlS5ObmwsLCAjKZDAEBAfjjjz8A/FfE5Ofno3PnzsjNzUVISAg+//xz3leIb5SI+fbbb4nyGzNmDPLy8vD111+Tir2hiFEoFAgMDCSubNXuKHY7efKkoIiJiIjAokWLyPMGDx5MVPqMGTNw9+5dAMCuXbvg7+/PudbS0hJxcXEICAgQbF2rbqqFFQACAgIQHBzMe8+wsDAEBwfzzgeUgx9nzJiBsLAw0gLx9fVFYWEhMVzatAjq6upw79497NixQ+14AXXU19cjJCQEt2/fhpOTE2/QVU5ODvEmjRw5EsnJydi6dSsiIiJIJSgkYpqSDqrGKTw8nPy+du0aiQt1bmK2O+x1dCeJxWL8+uuvJK3Hjx+Pc+fOwdXVlYT73//+N6+bVDV8f/31F0c4Ojg4ICwsTPB5zs7OqKurw4YNG0j8DR06lDMe6Ntvv8XkyZN5AwVVvS5sN5cmEaPKrFmziBeHva9Qd5JcLkd0dDTPC+nh4UEGFrObiYkJ1q5dy3lOXFwcxzMCcD0xMpmM8/WKKpMmTSK/zczM8PjxY7LPVkwNv2zSt7zrmz/18cSkpqZi7NixHJsya9Ys3piYXr16ca5rSvnTladPn+Lhw4e843Z2doJenoYDpUNDQxv1xAh1i+uTDo8fP8bRo0cRGBiIjRs3EiHJwooNTXZXoVBg5syZOH36NLF7MpkM48aN4zRAVBuSqvkTUIoY1jtibGzM6WorKCiAk5MTfv75Z3z22WfEy8J6PlRFTHR0tOAUBFFRUZz8xPZ6sFhZWZE6RqFQcISyoaEh6aKSSCRo164dAgICEBMTgylTpiA4OBjBwcEYPnw4ecaXX34JPz8/co/w8HD07duXeOZv3rwJ4L8iJjMzE1ZWVgCUwwEYhuF5k94YEVNXV8cxlhcvXkRycjKMjY3JsYYihu3bZhk8eDDvvuo8MVevXhXsCwaUngZ24OuWLVt4RoaNXPb+Bw8eRGBgINmcnZ3VVoxJSUmCmam6uprXH1peXg4/Pz8cOHCAFDxAmcCJiYkoKCjgZKpevXppnCvj0KFD2LNnD+fYzp07BUfJs+Tl5cHOzg6Ojo7EQNvZ2fEqiadPn+LKpweY1AAAIABJREFUlStISUnBmDFj8PjxY+Tk5OD58+caRUxT0kHVOEVERHCMExtOoUpCdQzH6xIxy5cvJxUdW6DZwhwdHS3YYlINn62tLeerCHt7e95nm4DS0KxYsQJubm548uQJXrx4AUBZ8bF5g523omGrrra2llNWWBGjauQaipgbN26QSmTYsGEICAhAUFAQ/vjjD06ZbuiJuXfvHrKysjjjwk6ePAlLS0vBMWOqwsLCwgLV1dWk26OhJ+bSpUsIDAwkYS8uLoZIJCKVNPu+vXr1wsWLF8n+gAEDsH//fowaNYrjGte3vOubP7UVMapdJ6mpqbyGxerVq3memIb5rCnl72WhTsQ0JCQkhIyJAvieGD8/P8GBwbqmg0KhIF6noqIi7Ny5EyNGjOB8PTVy5EgA6kWMTCbD/PnzSZe+ra0tHj9+jPXr12v8fHrChAmc7qSlS5fi3r17AIARI0ZwBuxXVFRg9+7dkEqlHA/byJEjcebMGY54HTt2LM/zCQA2NjaYP3++2vfJyspC27ZtkZmZiYiICI5X3tDQkFMfjx49Gn/88QeOHDmCJUuWQCKRkI0Vl126dOGIpMrKSgwfPhzt2rXDpk2bSLlT7U7Ky8uDjY0Njh49CoZheF1Kb4yISU9Px8qVKznHWKPEoq47iaVv3768gZMBAQFadyexili1S2fVqlUIDw/nXMsatbt37yIuLo7nxo2JiVHbt79161b8/vvvvPc8deoUunbtKtj9dPnyZVhYWHBaIAkJCcjPz+c8u3fv3igvL4dMJoOPjw9CQ0M5GdnS0hJbtmwh+yEhIfD09OS16Ovq6hAcHAwTExNs3LiR17rZt28fx8XIYmxsjOvXr2Py5Mk4e/Ysdu/ejdLSUsydOxeAsiWlTXeStunAGqfHjx/D3d2dxNHBgwfJFzSqxqm4uBgODg745ptviPjVRcTY29tj//79cHV11ep81ff8888/Sd86WwldvHgRt2/fxtSpUwXn3WHDJ5fLeV8XHTp0iDcu5NmzZzA1NcXq1auhUCjw5MkTUglNmDABsbGx8PLyws2bNwW/MAoLCyPiVCqVks/o9fHEsLCffqr7OkmVy5cvaxwTk5WVhSVLlnAGGrOoemIAZf89+8ms6tgHlqSkJEyfPh1//fWX4Pwaquhb3nXNn2y5PX/+PKytrSESibB69WqcO3cOLi4u5OujZcuW4fz581iwYIFg9wmLkIgRekd9y9/LojERU1ZWJvgF55w5c+Dk5MQ5FhAQgFGjRnEEh67p0JCAgACe3RowYAAAYRGTnZ2NhQsXomPHjiQuy8rKeGN2hBAaT3njxg385z//Id277BeLqqiKmPHjx/M+wzczMxNsrB4+fBi9evXizTWlyoIFC7B06VIsW7aM4zVqKGLGjh0LZ2dnODs7Y/DgwbwPTQCga9eunDq9qqoKcrkcJ06cwCeffEKmlGBFTGpqKvr164fi4mLU19e/2SLG3d0dly9fJvt5eXn46aefOOc0JmIGDhzIO6bOEyM0oE1IxZubm/NGkltYWGD37t04e/YsJBKJoFHbuXOn4Dv+/vvvvEG47Huq65NXdYECygS+d+8eeba/vz969+6NGTNmCF7PYmdnJ/hsVaKiovDBBx9g+vTpaj+93rlzJ2/gZHJyMv78808A/y2I1tbW8PHxIc8MDg7mGYOmpINYLIaxsTEOHDiAqKgoYmSzs7OJ16KhcSooKECfPn3IvrYipra2Fvv27QOgHIioC8HBwVi6dCl8fHywf/9+fPvtt/D390dubi7Gjh3L6edniY6OhoWFBZYtW4aAgADe5FgHDhzgfVIPKIWira0tqqqqMHDgQNJFxeat4cOHo6qqCmPHjiV92yyWlpbEyN65c4dM4tVwYK/qHCyqsCJGKpUiMjISxcXFpBWprYhhJ51Tx/3792Fubk4G3bLCXtUTIxaLER4ejuDgYMFBzHV1dZg+fToRL1u2bFE7YBLQv7zrkz9VOX78OOcrwPDwcJw7d07rGZVXrVolOOmnKk0pfy+LxkSMQqFAYWEhbxyZtbU1bty4ITgHl6rnrinpkJaWhmXLlvHinPXUqIqYsrIy+Pr64tixY6iursaAAQNIpS+TyRAVFYXp06dr/JBC3Uch8fHxxAbY29vz/lcVMZMmTRIUMUJiPSEhAQzDwNnZGQqFAo8ePeJ09wBK2962bVvetBqqIkYqlaJz584oKChAUlISDAwMsGfPHtTW1pKJWAGlJ0a1K1gkEpEuxoSEBHzyyScAlHnXwsICBw8eJF13z549A8MwyMjI4OTRN0bErFu3jjOIb+nSpaSfjGXhwoUajY3QwN7c3FxBESMSiXh9wWwmyc3NRXx8PLKzs/H111/zWnGzZ88mM37m5uZi9uzZHNf3xYsX1c6Iun37dnh7e6OkpISzpaamqp0vRCQS8Twx0dHRyMnJIQb14cOHaruSpFIpFAoF9u3bJ1jxNURVTLLEx8fD0dERrq6u+OqrrzgGTaFQwMnJCSUlJWTgKqAs1KqfjV+4cIE3ILgp6eDl5YXvv/9ecKZONi4cHBx4Ika121EkEmHFihW87ov79+9j/fr1nDy5fft2HDt2rNGxRyzp6en44osvYGJiAhcXFyQmJkIulxNBERgYiE2bNmHMmDHw8vIirtTY2Fh89913KCsrQ2lpKX799VfY2toiNjYWeXl5KC4uRkJCAsrKyiCRSJCUlETGODk5OeHo0aMoKCjguH5Z9zfbhx0cHMz55DIxMRFubm64efMmdu7ciXHjxpHrhw0bRrx3Tk5ORGgoFArEx8cjMDAQu3fvxuDBg7F9+3b4+fkRQ82mTXx8PH777TeN8cWmhSaCgoKwaNEi3vGTJ09yPDHqKCsrw5IlSzgu/ZqaGsydOxdbtmwRnLxL3/KuT/5kcXNzw/Dhw3melEuXLmHo0KG4fft2o2G1trZGZGSkxnOaUv5eFgcPHtT4JaU6Vq5c2ahIA/RPh7i4OGzevJnThcPy7NkzVFdX4+zZs6SBqfo5NKD82jY9PR0HDx4kHtH79++jZ8+eMDMzg5eXF6+OmzhxokbvWklJCfr370/S6PHjx0hLS+OMdZowYQLOnz/P8fSPHz9ebd1pY2MDAwMDvPvuu5gzZ45gPTJt2jTO+DFAKYxGjx4NDw8PLF26lJMWjo6OaNWqFVq1aoWhQ4eirKwMly5dQtu2bWFiYkI8MyKRCH379sX58+exd+9eODs7IyMjAwMGDECfPn0QGBiI9u3bw8jICG5ubvjggw9gamr65omYlJQUTn+do6MjMjIyeOeZmppq/Nb9u+++I7/lcjnc3d3Rt29fQVdvaGgoXFxcyP62bdtIV45CocDFixfx/vvvY+HChbxr8/LySOWWmZkJY2NjJCYmks3Pz0+tJ2br1q3Ys2cPYmJiOFtoaChGjBgheE1ISAhnjgYXFxekp6cjMzOT1yoUIjU1FQsWLEDr1q2bNFdARkYGevfujREjRnAKq1QqRU1NDbKzs7FgwQIsXrwYUqkUR48e5XwyfvbsWeLNYGlKOtTX16udMygmJgYLFixAmzZtEBcXR47n5eVh2LBhZP/q1asIDAzkpJ/q1rB7T9vZUlmEZiGdOHEi/Pz8yBc0UqkUS5Yswdy5c0l3TMPWcEREBFauXIkpU6Zg8ODB+O6779CnTx/06tUL27dvJy2+hIQEzrxI7Iyjmrx0dXV1nPEUrq6u2Lp1K0ljVQ9hfHw8xxM1d+5crFu3DrGxsYIegqdPn2LHjh14++23OeMZhGA9VprIzs4W7Mr09fXV2JqXSqUIDQ2Fh4eHYCUhl8tx7NgxdOzYEQsXLuR4cPQt7/rkT6lUij179sDHx0etx+Xq1avo1KmT2rCyWFlZCY6DUKUp5e9lsW/fPsFJDRvDyspKq+n5dU0HmUyGM2fOIDg4WO09Hz58iF9++QWtWrXifJbMolAo8Omnn2LmzJm8eW8qKiqwb98+uLq68oTV+fPnMWbMGEydOhXTp0/nbJMnT8YPP/wAc3NzYh+ys7MxdepU8pUVoOzWaei53bZtm8ZlVEpLS5GdnS2Y5+RyudpyWV5ejoyMDMGZw6VSKTIzMzV6DmtqaiCTyZCRkaF2xuaKigqSJ4WWiXkjRIxqhOfn56utKNLT0zUuSCbkLrt7966gktZmsThtFkisra3l3UvTui2xsbGCLZrq6mrBkfuAsiIUCkNJSQn5rr4xZDKZ1lOQa0J11lshamtrIZfLBQtMTk4Ob+6Hl5UO6hCKH10WY3sVPH369LUubBkXF6dxbpjG0lTXhQeFuH37dqPdIBKJRO+p0e/fv89rKaqSmJioVTdMfX29xmkRdC3vjdEwf8bGxmpVJoQaeUL3biztXnX504akpKRGp3oQ4v79+y9tKn3VdCgvL1c78aEqCoWCfL0lxOtaq4idrJHlZS1mmpWVRT5FF5rO4U3hjRAxFAqFQqFQ3hwcHBzQvn17rbvP/y6oiKFQKBQKhcJD20HkfydUxFAoFAqFQmmWUBFDoVAoFAqlWUJFDIVCoVAolGYJFTEUCoVCoVCaJVTEUCgUCoVCaZZQEUOhUCgUih68zvmemgOPHj3SKU5kMlmT45CKGAqFQqFQtKS+vh43b97E4sWL0aNHD97suP90SkpKsGbNGrJKvCqmpqZklujS0lLOxK7FxcW4ePEi5/y6ujpYWFiQ2cv1QWcRk5mZiY0bNwpOtfy6YFcjNjMza3RBw1dFRkYGNmzYgDt37ryU+yUkJMDKykpw7RapVAoXFxfO1PAUYa5fv46DBw/i9OnTKCsrQ2JiouB5z549460cfffuXc56I5GRkbwVU/V5FkU3NJUFfWnMbvn7+8PDw+OlPe/vQle79E8Jt7Y0xZampKTg3LlzOHbsGHr06IHHjx+T2b937txJ7IaHh4deyyg05FWUg5eFt7c3rKysUFNTA29vb4SFhUEkEmHs2LHw8PCASCTCtm3beIvrrlq1Cg4ODpxjT58+xVdffaX3u+gsYu7du4d3331X47oSrxKFQoERI0agpKQEsbGx+OSTT9ROnS2TyVBYWPhK3uPOnTto3779S5mOWaFQIDw8HAzDCK7SnZWVBSMjI1haWjb5WU3hVcbny2DDhg3YvHkznj17BrFYjG7duqk1Jhs2bMDq1as5x2QyGRYvXowPP/wQISEh8PX1RdeuXTF79mxeHtPlWX8nb3qaNaSxsqAvjdmtCxcu4PTp0y/teX8XjdmlhvnhnxJubXlZtlR11WhAudgji7+/P/bv39+k+zelHLyOMl9fX08aeFVVVZBIJJBIJJgzZw5iYmIgkUjg5ubGW+IlPz8fJ0+exJMnTyCVSsmWmppKfm/cuBEhISFav4te3UndunX720RMcnIyOnToQPY1zSjo5+en1QJh+tK5c+eXuqZEq1at1GbYVatW/e0i5lXHZ1OQSqVo0aIFZ82TuLg4QWFRVVWFTp064b333uOtLXP48GF8+eWXZD8qKgoMw8Df31+vZ/3dvMlppglNZUFf/k679TrRZJeaa354mehrS9PS0siin/369UNiYiIOHz6M+Ph4DBw4kJzn7+//0myBPuXgdaRxYWEhsX/l5eUQi8VITEzE9OnTERYWRuKJ7U5KSUnhdD+FhITg7NmzuHHjBq5fv46+ffsiMjKSbJrWpGqIRhETGRkJJycnngu2R48exBhcu3YNYrGYPFShUCAiIgLOzs5ISEgAoFxw78qVK8jKysLZs2e1XrSwIVlZWbC1tcWHH34IsViMJ0+eAFAuUHb8+HE4OzsTdRgREYG3334bBw8eJCs3KxQK3LhxAw4ODhCLxUQAXblyBWKxGGVlZSgrK4NYLMb169fJcx8/fgwXFxeEhoZy3r1r164QiUSIi4uDs7Nzk9WvoaEhkpKScOHCBfj4+KCiooL8t2bNGlLwhMLLhu/q1atIT09HUlISjh49SlYRDQsLg4uLC2fBNKG0UhdeXeJTKL11fTddKSsrQ8uWLbFu3TrOQLGbN2/yzj1+/DjOnDmD9u3bw93dnfPfkSNHOCImPT0dDMNwVvrV5Vn6oK7cCaW7XC6HWCxGVlYW7t27BycnJ2I4dEkzNn2Kiopw6dIl3mrP+l6nL+rKgqay2ti7aLJbaWlppF9eXXnXB03pw76zPjZJoVDg2rVrOHHiBKKjo1FfX08WolRnl4Tyg2q4dbEH+qKLHWhKHGj6T9WWqguzEE+ePEF2djaSkpLQsWNH5OTkICcnB8XFxRg0aBA5T52IUVcPqssDgOY6Qdt6T119oQ+pqak4ffo05s2bh127diE3Nxe+vr4QiUQQiUSYN28efHx8IBKJ4O/vjx9//BFPnjxBVVUVxowZgwcPHuDWrVucBYLr6+sxYsQIvd9JrYixtrZGaGgosrKy8N5773FcjqwxUCgUWLBgAU6dOkVWWd64cSPu3r2L2NhYfPzxxwgODoafnx9atmyJZcuWwdjYWO2y3o2Rk5MDBwcHfPTRR4iMjMTTp0+Rn5+Pzp07Izc3FyEhIfj8889RV1eH+/fvo0OHDnByckJSUhIA5dLtp0+fRlFREYyNjWFubg4AKCgogKGhIe7cuQOFQoGAgAB8+OGHAIDc3FxYWFhAJpMhICAAf/zxB3mfrl27wtraGvv378fEiRMxbtw4vcLFYmhoiMWLF8PS0hKdOnVC//79STcGW/DUhRcAAgIC0Lp1a6xfvx4HDx6EqakpOnXqBAcHB9ja2mLYsGH4+eefyfOE0kpdeHWJT6H01vXd9MHOzg4Mw6Bv374cY6eKQqGAiYkJFAoFli9fjv79+3OMhqqIqaiogKmpKbp3785bWV2bZ+mDunKnLt1PnDiBFi1aYPny5di5cycmT56MsWPHAtA+zRQKBZydncEwDNasWQNzc3N8+eWX6N27N8lb+l6nL+rKgrqyqs27qLNbcXFx6NmzJywtLTWWd33QlD7q4hXQbJMAYOXKlXB3d0d9fT1GjRqFr776Cvb29gDU26WG+UE13IBu9kBfdLEDTYkDTf+pihihMDeGo6Mj3n77bRw/fhzFxcUAuN1L6kSMunpQXR4A1JcDbes9TfVFU/jjjz8QGRkJQNl91XAF+YqKCp43KCQkBE5OTpwGIfCKRExUVBQmTJhA9p2dnSEWi8l+jx49cP78eWzbtg2xsbHkeExMDKZMmYLg4GAEBwdj+PDh5D5ffvkl/Pz89H5RlmvXruHTTz8l+5mZmbCysgIAvHjxAgzDIDc3lzyTjchbt26hR48e5Lrc3Fy0bNmSJMSHH35IBsPduXOHFJbw8HD07dsXBQUFALit7a5du+LMmTPkmvbt2zcpbIaGhhz13K5dOwQEBAD4b8HTFF4A6N69OxnsXFRUBIZhkJmZCQAIDQ1F586dAahPK03h1SU+hdJb23drCqGhoejatSsYhoGxsTHPK3Dz5k0cPXoUgPJzQIZhEBMTQ/4/cuQI3n//fezcuRPvvPMOrK2tIZVK9XqWrmgqd5rS/YsvvsDZs2cBKMd+tG3bltxDlzRr27YtwsPDASg9UC1btkRgYKDe1zUFTWVBXVlt7F3U2S0AWL16NSwtLTXmf31Rlz762qSamhq0aNECaWlpAABXV1f069eP3EeTXVLND6rh1sce6IsudkCfOGgsflhbqqm+UkdVVRWGDRuGPn36ICEhAV999RUeP36MwYMHk3P8/f15g1dZGtrFxvKAunKgbb3XWH2hLw1FTL9+/Yg3RiQSwdfXF0OHDuVdJ5VKceTIEdTU1BBPW3h4OHr37g2xWAxfX18y5lVbBEWMjY0N5s+fr/aiHj16YMiQIZg2bRqvFbtkyRIyyEcikRBXZpcuXThCSF8aihgAyMvLg42NDY4ePQqGYYjLTDUxd+/ezem3BJSFad++fQDUF5bKykoMHz4c7dq1w6ZNmzjuPNZtCwBJSUlo0aJFk8JmaGjI6f8cPXo0afmoth7UhRcAvvnmG9KaqK6uBsMwkEgkALhxpy6tNIVXl/gUSm9t362p1NTU4M8//0TLli0xa9Yszn+mpqZYs2YNtm/fju3bt6NDhw4cD5CqJ2blypXo3LmzRnGi6Vm60li5U5fuqnH9+PFjTj7UJc3atWvH+cLqhx9+wJ9//qn3dU1BU1nQJGI0vYs6uwUA69evh6Wlpcb8ry/q0kdfm1RfX4/3338fly5dAgC4uLhgypQp5B6a7FJDEcOGWx97oC+62AF94qCx+GFtqab6Sh07duxAdHQ0ES137txBamoqrzvp8OHDgtc3tIuN5QFN5UCbek/TeU1BVcSUl5ejf//+PBEjNO7I2dmZhCcvLw8KheLVeGIOHz6MXr16cQq66mdePXr0gLe3Nzp37owDBw5wXnDw4MGc6549ewZAWbBUEy8rKwtr1qwhrSZtaZjJU1NT0a9fPxQXF6O+vl5tYtra2uLTTz/lvNuAAQPg7OwMQFlYbt++DUCZMT/44AMASuUtl8tx4sQJfPLJJ5g+fTq5XtVYJCcnE2Ohb9gaZtixY8eS92MLnqbwAkDPnj21MhDq0kpTeHWJz4bprcu76RN/1dXVpPXJYmdnBwMDAzJCPicnB+vXr+ec4+/vj9atW5NxCqoipqamBn379sVPP/3ECac2z9InDJrKnaZ0V43rtLQ0tZVWY2nWrl07zriAcePGwcPDQ+/r9I0HQHNZUFdWG3sXdXYLUH5tZmlpqTH/6xsWdemjr00ClB6LESNGwNfXFxYWFsSTwT5PyC4B/AqODbc+9kDf+NDWDjQlDjT9x9pSTfWVEDdv3sTx48cBgCNaAKB///5IS0tDWloa7O3tYWdnJ3iPhnaxsTygrhxoW+81Vl/Y29tj//79cHV1VRtuIVgR4+bmhuLiYp4HKycnhyNisrKycPjwYUHvzCsRMQkJCWAYBs7OzlAoFHj06BHHBdatWzdcvHgRcXFxaN++PcnESUlJMDAwwJ49e1BbW4vMzEzyPX6XLl2IMmYpLi5Gjx49YGNjo/ULX7lyBf/zP/9D9g8ePEj6mJ89ewaGYZCRkYHKykoySr+wsJAYj7/++guAcpKdzp07Iy8vD4Ay4dnPuk6cOIH27dtDoVBAJBLh4cOHJF4++eQT8uzOnTsjLCyMhJ1hGJIZ9QmbaoaVSqXo3LkzceOuWrUKFhYWGsMLAF9//TWZUKiqqgoMw5AwXr16lby/urRqLLzaxqdQemv7bvrEX3V1NcaNG8f5pC80NBS9e/cm++vWreP13dbW1uLDDz/Ezp07ASjFiKoRTUlJwVtvvcXpx9XmWfqEQVO5ayyfX758GYCypa+aD3VJs3bt2pHBxPX19ejWrRtycnL0vk7feAA0lwV1ZbWxd1FntwBl3rCwsNCY//UNi7r00dcmAUqPYn5+vtrnqbNLqvlBNdz62AN940MXO6BvHGj6j7Wlmuqrhjx58gRXrlwh+6qfVANAnz59OCJGXXw0tIuN5QF15UDbek/TebW1tcTj01DUN8bOnTthZGSEGzduAAC+//57zhxb/v7+PE/M1atXYWJiwrvXKxvYa2NjAwMDA7z77ruYM2cOGQwUFhaGtm3bwtLSEoWFhZg0aRLeeecdeHh4oLa2Fo6OjmjVqhVatWqFoUOHoqysDJcuXULbtm1hYmLCU7qXLl3i9AlqIicnB2ZmZmAYBn5+fnj+/Dnu37+P9u3bw8jICG5ubvjggw9gamqKyspKLF68GF26dIGvry8AZd/oV199BTc3N6xatYr0UwNKN2HHjh0xb948ODk5oU2bNnB1dYVIJELfvn1x/vx57N27lyhkkUiENm3aYNmyZXj69Cn++OMPMAyDU6dO6RU2ADAzM8Po0aPh4eGBpUuXkoydkZGBAQMGoE+fPggMDFQb3qtXr+Ktt96CpaUlJBIJcR/a2toiLy8Py5cvR6tWrRAaGgoAgmmlLrwAtI5PofTW9d10jb/q6mr06tUL8+bNg6urKxwcHDBy5EjSn3zixAm0adMGHh4e5CsFAIiOjsbHH3+Mdu3a4X//938xfPhwGBgY4MSJE2QmTg8PDxgYGGD9+vVIS0tr9Fmq6JoH1JU7dfk8ODgYrVu3JpNi7dq1i5QPXdIMUAoAU1NTXL58GatWrSLXNOU6feNBXVkA1JdVTe+iyW4lJSVh0KBB6NOnD9zc3NTmf33CIhaLNaaPPjYJUA4kbdGiBd5991106NABQ4YMQXx8fKN2STU/pKenk3CnpKTobA/0iQ9d7YA+caDpP1Vb+vDhQ8Ewa0OfPn04+6qTukkkEsEvLdXVg5rygLpyoG29p+k8ANi+fTuOHTuGvXv3ahVuFisrK6xZs4bsN/yoJSsriydixGIxFi9ezLtXQxFTUVHBa2xqQuMn1qWlpcjOztY4F4sQUqmUfDanDeyn0vpSUVGBqqoq8mzVz+waZqaysjIkJyejurqac1yhUODJkyeoq6tDZWUl+aa9pqYGMpkMGRkZZCS6LugatvLycmRkZPAmCVJFXXj1oWFaaQqvLvH5stA2/uRyOUpLSyGXy5GcnIyUlJRXtq6Jrs/SNQ+oK3f6pLsuadauXTvExcUhLS2NPOdlXMfyssqCurKqy7uoQ9vy3lSbpYquNqm8vBw7d+5ERUUFnj59ikePHuHSpUtYtWpVo88Syg+q6GIPVHmZ8aGKPnGga/zoWl8B4Hldm4omO6quHGhb7zVmN3QZRMtSXFxM3lUmk2HMmDEAlI1JR0dHdO/eneeNEovFWLBgAcrLy+Hl5UXGz4SFhWHjxo1k//Dhw9i8ebPW9puunUShUAAov+xpbK6Ml3ndq+BNepdXxZ9//olFixZxJlu8du0ab12afzKa4uB1xI/QukH/X5HL5Xj06BHnWEZGhqAo1NQ41xcqYiiU/+fU19fD3d0dDMNg48aNWhtofa97FbxJ7/KqyczMxKxZs/DFF1/gxx9/xOzZs3Wapv2fgKY4oPHz/wv7I8ozAAAgAElEQVQqYigUCqWZomtX/z8RTXFA4+efDxUxFAqFQqFQmiVUxFAoFAqFQmmWUBFDoVAoFAqlWUJFDIVCoVAolGYJFTEUCoWiBnZSMG0pKip6RW9Cobwa1H32LJfLmzQHmTa8jPKik4hhpxh+VcjlcsEJqvLz8xtdeOzWrVuNjkRXN0EYu26HNjRc0TglJaXRax49evTKJl5TR8NVerVBJpMJxqG2GVkikehs9F8FLyOd1VFbW6vTXAfs9OHa8uDBA11f6bXEu7rJzPLz8znr0mhDdXU1kpKSdLqmvr7+pSxcByg/wW2Yhq6urli7di0v37z//vswMzNTuzBgTEwMIiIiyP4777yDjh07IigoCEVFRZg3b57GyeW0RSKR6DV5nyZ0zcvaEBsbi9ra2pd6T1VeZj7UB13yYVPtaVOIjY3l1VXq6Nq1K9atW8fL42VlZXjnnXdw4sQJXj7x9PREp06dOOs6NaSoqIg3kd6rKC86iZj9+/fDy8uL7EskEo0LZmlDaWkpFAoFIiIiMHToUKxduxY5OTmQy+WIjIzEypUr0bt3b2zbtk3tPWQyGT766CPO1OQsqpmosLAQU6dORXp6OuecS5cuYebMmZzJkdSxdu1azrTQAwcOJIsHquPBgwdkRsPKykpUVVWRxI2OjsaUKVNw5MgRzjWHDh3C06dPASjX17h06RJnbQqxWIxjx45x0kOVvn37ksKSkZGBoqIiXL9+HZmZmWSNkIazQ+bm5mLGjBmc1UhFIhHMzc2xf//+RuPm119/xaJFixo972WETxNNTWfVQlRQUIBr167hyJEjMDU1xZdffgkHBwet32Xx4sVai3+FQoGOHTuSVXu15XXE+9mzZ7Fu3Tre/WJjY7Fw4UK1z5PL5YiJicGiRYswceJEKBQKhIeHo2fPno0ac9XVqAsLC9GuXTu105FHRUVhyZIlmDt3LiZNmgQjIyOMGjUKkyZNItuIESMwceJEMAyDGTNmcBoWnp6eYBgGo0ePJjbj+fPnYBgGDMMILqUAKFc6ZxgGXbp0QXZ2Nrp27QqGYeDu7g5LS0tyfVMrWG9vb87U9g0rUqlUKlihvMy83Bg1NTX47LPPeLZMiNedD4OCgsiqy6qYmpoiNTVV47vqkg9Vaao91ReZTIYuXbqoXUm7IWweDQoK4hyvra0l/6lOIHn58mVERkaS/yZNmoRvvvkGXbp0QefOncnGMAx69OiB0tJScu2rKC9qRYxcLsfp06chEomwdetW+Pj4wMPDA+7u7li2bBlcXFzg7u7OWRhSH3744Qd4eXlBLBaTit3ExASOjo6Ij4/nVThlZWWIi4vjbMeOHcOECRN4xx0cHDBixAhOQXZ3dxfM7HPmzOGo/IaqlDUQGzZs4FQyqqt3nj59GjExMWQ/IiICwcHBEIvFOHToEFxdXWFjYwMfHx+MHTsW586dQ3h4OLKysjgJDSiFT//+/QEohY+QqnZ2dsbRo0fJvlQqJWEfNGgQDh06hFOnTqFbt25ISUnBjBkzkJycjOvXr6Nnz568BdIKCgowefJk3nM2bNggaABUSU9PR4cOHRAaGopr166R7ffffxf0CukTPl3QN50B4LvvvsPp06dx4sQJnDt3DjExMcjMzFTbErazs0NYWBjEYjFWrFjB8YosXbqUPINdB0kdUVFRggukpaamqhU2rzPeVQ0vWy7v3bvHqVxZYVxWVoazZ8/Czs4OvXr1QlxcHBENy5Ytw4ULFzj3Fppu/fjx41i8eDEUCgWys7N5i+6pUldXh3/961/ECDa2de/eHdu3b+fc48svvwTDMEhPT8fevXvh4+MDhmEwZ84cAMryYWtry3v2jBkzwDAMrKys8O2334JhGERFRYFhGHz44Yfw9fXVqmLXxKlTp+Dp6Un2lyxZAm9vb1Lhb9q0STC/65qXm8KRI0ewevVq2NvbN9qafl35kKW+vh59+/YlC3GyfPHFF416+XXJh6o0xZ42BTc3N5ibm5MVphuiUChQXV1Ntnbt2oFhGFy8eJEjauVyORiGwbfffsu5/qeffuKUpZkzZ6otZwYGBmQVcpaXXV608sQkJibCysqK7M+cOZOz2B2rqPWBzciq/PLLL2qNdl1dHRISElBYWIjCwkJIJBL0798fDx8+JMcabqpCqLa2FhKJBKWlpfDx8eEo/5CQEHh5ecHe3h5TpkzhFIQBAwZAKpVi48aNcHJywu+//46QkBAiYry8vDBnzhxegh0/fpx0YxgbG5MVdhsumCWEOldddnY2amtr4ezsjHPnzpHjMpmMxM3o0aPx/PlzDBw4ELGxsXj27BnMzMwAAC4uLjhz5gzvvi9evMCAAQN4LaJ58+ZpLHQKhQKTJ0/G7du3OYUjJSUFHTt25Cw935Tw6YK+6Qwo01oXjI2NiVAZNmwYamtrYWxsDJFIhClTpiAgIACBgYEYOnQo8T5UVlbCy8sLYWFhpIU2atQosuio6ubm5kYWvVPldcS7TCaDj48PQkNDcf78eQwdOpSs4s22lk1NTSEWi+Hg4IDvv/+e152gasjr6urw448/csJ9/PhxdO/enbf4nlwuR+fOnZGUlIS7d+/C2NhYYzqoenZYQcKyaNEiMAyDuLg4Tjy0bt1aa+HDbitWrOAZcm02Td7kxjh9+jQ8PT0hk8mQnp4OKysrTjr6+/sLGn5d87K+5OXloX///qioqMCLFy+wZMmSRruVXnc+vHjxImclagD47LPPGg2brvmQRV972hSysrLwzTffoLS0FNXV1bCwsOB1NcvlcrKQstBmb28PQGlfGIbB119/DXt7exKfy5cvB8MwOHjwIFxdXUlXE3v9rl27yH6HDh1QWVn5SsuLoIipqamBt7c3x9AcPXoUIpEIvr6+nMWbPDw8MGXKFL37ffv160fuNWvWLNy6dUtQxGRlZQleb2tryyu8tbW1Gl346vo0z5w5ozbi2OXMN2/ezHm3MWPGwMXFhbR6G3qOEhMTMX/+fFRXV3PuPXHiRM55Qn3TUqkU9fX18PX1RXh4OCkETk5OiIyM1FjJT5gwAQ4ODrCxsSFLxLMiZu3atYJ9tUVFRWpbDpric8+ePVi+fDmnr7OyshKDBg3C+fPn1V7XlPBpgz7pDACDBw9W+9/z5895xtHExISIGFbUsobOwsIC5eXlKCkp4a3qqkp8fDwsLCwAKCuEGTNmNOq5ed3x7u3tjT/++AMAMHr0aABAXFwcaQHv3r0b2dnZAJTdGImJiUhMTMTw4cMRGBgIZ2dnnDp1CpcvX4ZMJkNYWBi5Ljo6mjynpqYGJSUlKCkpQUxMDEpKSuDr64tly5aR4yUlJfDx8eF5MFm0ETEA8Ndff2HHjh3EcB45cgR2dnbw8vIixzZs2EB+W1pawtnZGTExMfj8888xZMgQDB8+XKMxHjFiBDZt2oQtW7bovFBiQEAARCIRLC0tsW7dOgQEBMDOzg5Lly7VSsTompf1oa6uDpMmTeLE7V9//YUpU6ZoXDTydeTDxvj888/Vjidraj7U157qS2VlJcaPH48HDx7g7t27KCoqQnJyMoyMjHhj8+RyOfz9/REZGYn27duDYRjS1aNuGzt2LGpra2FtbQ2GYbBnzx5ERkaSjT0vICAAP/zwAxExgNJj/KrKS6OemODgYF43TVxcHK5duwZ/f389o/u/qLrmTE1NUVhYiLlz58LZ2RlisRhbt26Fp6cnfvvtN57rPyoqCp9++ikuX77MUbp+fn7o3r07x0VZVFQEHx8feHl5oUePHqiqqkJOTg7HeC5atIjjYVJl/PjxAPgihj3OwsZJZWUlfHx8IBKJ4OzsTLrkPD09YW1tjYkTJ0IsFiMsLAz+/v4YMWIECgoKUFpaCm9vb9ja2sLc3BwAOH33CoWCjMFRLeQNhefy5cthZGSEsLAweHh4ICgoCGZmZigrK0NgYKBgGCUSidqWg2pFqUpQUBCCgoKgUCgwceJEhISEoKamBkZGRhz3N4u+4dOWpqYzAHz//fcQi8VwdHTkxcWwYcN4y9b//PPPPBHDdkH8/PPPqK+v1yhi6urqMH36dHKPFy9eCBo/VV53vMvlcowdOxZ37tzB06dPMWrUKADcykO1IVNbW4uUlBSkpKTgo48+Qk5ODjIzM8nKtgqFAr1790Z9fT3i4+M5zyosLERwcDBiYmLINm/ePPz555+cY7169UJ0dDTPPV5dXY0vvvgCDMOQ/QULFoBhGMTGxiIyMhLfffcdp4JlDejcuXPxr3/9C926dSPHAKBHjx5gGEawsjp16hTPEH/33Xfkd7t27RAaGqoxPRtj4sSJcHJyIvsrVqyAh4cHyZdbtmwRFDG65mVdqampgbW1NUJDQzljA6Ojo3HhwgV06dIF+/fvJ43Q150PCwsLERkZibCwMCxdupTjrauoqMDIkSM5Y15U0TUfNkQfe6oNNTU1EIlEnGNlZWX46aefyHscOHCANNRiYmLQpUsX7Nu3j4yN3L9/P9avX481a9aQfPrrr7+S3+bm5kT4MwyDffv2wc7ODvfu3cPq1au19qYYGhry3v9ll5dGRczw4cMhkUh4W2RkJObOnav1g9Sh2p3Eipjx48eTLqpFixYJuh2jo6Oxdu1a/Pjjj7z/JBIJZs2aJfg8iUTCGQSZkJCATZs2cQbfCtFQxOTk5CA5ORnjxo3jeDV++eUXQa9Uw0rJyMgIABAWFgY7Ozve+WKxmBj84OBgrF27FiKRCAcOHMDs2bMBCBfyuro6PHr0CHPnzkVFRQUyMjJQXl6O58+fw8zMDDKZDLt27RL0/JSVlQmOo8jOzoZCoYCnpyfHHfvixQtO2qSnpyMlJQUTJkz4P/a+Oyqqa21/YjTJTftMcu9NuUksN5qoUWOMiZUYu2hsWBATjSgIooDYG8uK0qQIAiKKgiAgRZA60kRBBQUlFAEB6UhvUmaY5/fHrLO/OXPOmQJo4vc7z1qz1syZU96997v3fs77vnu/8PPzY9ynL8qnKnrazgAwefJkANI3Ptm3tPLycvz3v/9lWNt+++03BAYGQigUEosdRWJWrVoFAITEyK9SE4vFmD17Nm0QbGhooOlLdXU17Zq/ot7Pnj2L3bt3w9jYGHFxcfj+++8RHByMc+fOYfPmzYiMjMTKlSsZb96+vr54++234erqisLCQnR1dSE+Ph5dXV2YPXu20ngECt9++y1jddmwYcMgEonQ1dWFpUuXqm2uHjt2LHFhKTovPT0dAoEA//znP2nPz8nJweeff07OCwkJId/b2tpQUFCAn376CQKBAJ9//rlK5WRDRUUF3nrrLRgbG2Pjxo3o7OzE1q1bVbLEqKvL6qCgoACHDh1CdXU1AgMDsW7dOvLf+vXr4e/vj6dPn2LRokVYvHgxLSnny9LDzs5OFBcXo7i4mOEGys/Ph5GREatrnQuK9FAe6oynbO5lrs+FCxewd+9eEkOalpaGffv20UjkqVOn4O7uTn4XFxdjzpw5mDx5MjIyMvDmm28q7R/Pnz+HQMCMiaFIzJUrV1BaWorMzEzadS0tLWhpaWH0mRfVX1QiMfJMUigU4vz58zA2Nlb5QVwYNWoUueeMGTNQUVGBb775hhADeRIjkUgQEBCAK1euAKC7o6iPj4+PyiQGkE4K//3vf7F//37W80+cOIEvv/wS+/btw9GjRxEYGIjc3FxIJBK4ublhxYoV0NXVxfr16zF9+nSy7JoiOjk5Ofj555+Rk5OD/fv3IzY2lpAiVUhMdHQ0+R4fH0/entg6+cmTJ7F48WISaLp8+XJ4eXmhpqaGuJOSkpJob2CBgYGIiIhAeHg4wsPDIRQK4eLignXr1tHa3M/PjxGQSaG5uZmYkO/fv0/7j82k3NPyqQp121kWU6dOJXLLBhHu2LGDdbWU7BLcWbNmITg4GNra2oiMjISGhgYCAwMRFBSE1atXY9myZbSJ29PTE6NHj0ZjYyNx0169ehUTJkwg+rx69WpawLgsXka9U1mBZ82aRd6Qfv75ZyQnJ5NnpKWlYd68eQxyvGzZMkybNg23bt2ClpYWxGIxhg0bhidPnmDu3LmIiorCn3/+yVo2CgUFBZg+fToSEhJoLwyy8QwikQiXLl1CbGws4uPjyeBIBTrPnz8fAoEA7u7utABoyuUoEAjw1Vdfob29HQKBNPA3ISEBAoE0JoByJVEwNTUlz7CwsEBHRwdtUKbaSywWY/r06TTXlrrYt28fTExMcOHCBZibm6OwsBDGxsYqkRh1dVlVVFdXIyEhgfwOCgqCjY0N+b1x40aae0neff2y9ZBtPLhy5QqxMKgCVfQQ6JvxVFU8ffqUtf/Y2dnRSAwFNrJF6ayXlxcuXryI48ePQyAQQEtLi5XEUNabpKQkiMVi/Oc//6GRGNn7Uu6kF9lflJ45adIkmt+L+ly+fJkW7CuLhoYGlfatoN7GKISGhiInJ4fGmOVJTGVlJc1NxObzVccSA0g7i4WFBQwNDWFqakpTULFYDDs7O86YHAryQYmAVMFu3LiB3NxczJgxA3l5eSgtLcWzZ8/UIjGxsbG0Tm5hYUHkpjp5c3MzLl++DCsrK3IuIFW4zMxMVFdXExIDSN8o5GMuoqKiMHHiRIjFYqSkpMDMzIxWD/n5+axlb2hogK6uLn788UeyGov6xMTEYPDgwYzgWXXLJ/ssVXRL3Xam0NTUhIULF5LfVlZWuHv3LlJTUzFt2jTWa3777TcaiaHQ3NyMjRs3cg5UPj4+CA8PJ2/LsmVU5k6iznvR9V5XVwd3d3fcu3cPO3fuxMyZM5GXl4dFixZBJBLBzc0NgHS1zM2bN2nPSkhIwLlz5zB//nwAwOXLlxEaGkrM/tTxVatWMaxNstDR0cG1a9eQmJiIlStXQiwWo6mpCcOGDWM9n1pV8eWXX5JjXDExgNRtIBAIMHfuXLKsetGiRQCAjz76iAy0VNC+u7u72lYfeRKkKsrLy2FgYEACeymwuZPkl0v3RJcB1fuYLLy9vWnB55s2bWKtawovUw8B+niQl5eHyspKbNmyBSkpKTh06BDNSsQFdfWwp+NpX4CLxABSlx61QojtQ/WVgQMHKrTEJCUlYebMmcTd1L9/f1YS86L7i0ISI5FIYGlpicTERFRWVuLw4cPIz89HZWUlMjMzWQdnsViMgQMHqrQMraCgACYmJrRjO3bsoK2q4HInURg7dizDSuTv768Sieno6IC5uTn8/f3J/6tXr8bOnTsZ1+Xk5MDf35/VKhUTE4PZs2fDx8eHcZ2WlhYSEhKgqamJoKAgHDt2DI2NjcQVFx4erpDE5OXlwcPDA3p6ehAKhbCxsSHR32yTfExMDDmXssQ8evQIVVVVNBIzevRohttr+/btpC5SUlKwfPlych9fX1+cOHGCxuSbm5tx7tw5WFlZISAgALt27WKtc/lJuqflU0e3etrOcXFxOH78OPnd1dWFxYsXY9y4cZz7SaxZswZlZWVob28ngYaA1PQdFBQET09PhlXk6dOnKC8vZ60fZSTmZdY7Rc7v3bsHc3NzoguHDh0CABw9ehQxMTH49ddfac/p7u6GhYUFuru7aUHsq1atIpYoisRERUUR95s8bt++Tc4DpIH09+/fR2ZmJjQ0NFivoYIMZV+yFJGY2NhYCAQCWFtb4/HjxxAIBGS5MhX0++GHH9Ku2bZtG1atWgV9fX3ykR+Ef//9d+jr68PMzAx2dnaws7NDYWEhAMDe3h6Wlpasq85kcezYMTQ3N+PKlSs4f/48Oc7mTpLfF6QnuqxOH5PFgQMHEB8fT35v3rxZJRLzovWQAjUeZGRk4Ny5c2hoaMD48ePR3d2N+/fvK7XO9kQP1R1P+xKKSAwgXXAycuRIaGpqEn1duHAhjhw5gqNHj2L+/PkkdkWexMgHAFMvJVwkBuhdf3F0dGRYmWWhkMS4uroCAMaMGYPW1lYIhULSkMXFxbh58yYOHjxIC5aSSCQ4fPgw6wAqDw8PD9q6/YqKCqxevZp2jjIS89NPPzGOcVli0tPToaenBz09PQQFBcHPz4+xo2BLS4vCiH51kJOTQwYRalIyNjbGpUuXSHBtWFgYJ4nR0tKClZUVbt26hejoaABSfyrlrmIjMbKmWUBqibl//z4qKyuxdu1a+Pr6YvTo0Vi8eDHtura2NsyZMwcODg6or69nvDnIo6GhATdu3CBv+klJSWpPpuqWT1Xd6k07Gxsb0yLinz17hnnz5mHChAkICwtjfd7KlSsREhKC+Ph4EhNTX1+PtWvXQiKRoKGhgaw8YoM6JOavqHdAuiM2FShoY2NDzL/UMmX5VQTZ2dlkLxKqToKDg2mxP9RYIpFIMHz4cIashYWFmDFjBu0tmSKDQUFBJDBUFhKJBF9//TUEAgHNzK6IxFDLTX18fBAVFQWBQID9+/cjIyODNsjKEgJ5nDlzBgKBAFOmTEFcXBwEAmlQI1sAeWdnJ06ePAlAah3hwpMnT0j8xeXLl3Hu3Dny35YtWxgkhloaS6EnuqzO+E2ho6MDv/zyC20eUIXEvAw9pFBWVoYJEyYQnTExMSHPBKQWVK6Jsid6qO542tdQRmJkQVlcgoKCoKenh19//ZW0JRuJ2bp1K+kTAQEBZANJgUCA119/nZwnS2LkoWp/kUgkSElJYWxdIgtOEuPv7481a9YAAI2BUg1PbUJkamqKvXv30q51d3dnrMdnw44dO2i+SwMDA4ZZ+Y8//kBWVhbnPdgCe8vLyxkkpqKiAiNGjEBZWRm6u7tx6NAhmJmZISoqCtnZ2SgoKMCTJ09QWFiIhw8fQigUqr0csrW1lcgqkUjg7OyMhoYGsp8BIH2j2LlzJzHlXrt2DadOnWLcy9PTE+PHj2dl6pQbyNHRkdHJIyMjGZaY5ORklJaWEkvMn3/+yXAl7dmzBwUFBWhvb4eJiQlWrFiBDRs2sG5CxrbTakJCAnR1dcmyWtkP214VPS2fMt3qTTvX1NRgz549AKSWBE9PTxgaGqKkpATNzc3Q0tLCrFmzEBQURHNpHjp0iJjfW1paUFZWBn19fdqyxgsXLmDp0qWsqQ+UkZiamhrWFUfAy6v35ORk7N27F2KxmOhRZmYm1qxZg8DAQCxcuJBzb6dp06YB+N+UCjU1Neju7ibB7QAzfqeoqAjr1q2j5Vapqakh7aWlpUWzTFDy//zzzxAIBAwrG0VU5Ceq6upqVlP277//Tr4HBQWR76NGjaIFTzc2NmLLli3kf2pBguwS7W+//Ra7du2irQo0NzeHq6uryquDLl68SF4qAemGgfLuJNlxpKe6DKg+flPYtm0bzQoDSEmMohWAL1sPMzMzMXjwYDQ0NMDa2prx4njnzh188sknuHXrFu14T/QQ6P142lvY2NjQ9EUWzc3NmDBhAufmkAMHDsQXX3yBBQsWEP2VhaGhIekXgPRFnLp28eLFmDRpEjZu3AiBQMCIF+pJf3F0dGS0iyw4ScyaNWvIoDN+/HgYGhrC2NgYxsbG2Lp1KwYNGoSnT5/i2bNntACx5uZmldbA5+bm0paZOTk5EVOrLHR0dBR2hu+//5587+7uhoeHB8aOHcvKeuX3RMjPz4eVlRXWrVuHBQsWYObMmdDQ0MDUqVNx8OBBSCQSdHd34/LlywgPD1caNW5tbY0dO3ago6MDLS0t6OjoQElJCdatW4eNGzeipaUFZ86coa0ICAoKIm9lshCJRJxxOCkpKVi3bh3efPNNxtvO9evXabuKurm5oaCgAEVFRTR3kizCwsIYg3tGRgb27NkDDQ0NjBkzBkOHDsUnn3wCfX191ok4OjoamzZtYp1MqdUGvS2fqrrVk3YGAGdnZ9TW1iI4OBiOjo60rbYphIaGYtSoUdiwYQMjbqClpQUBAQE4f/486/4bnp6eWLBgASNVB/VmTAUBRkdHw8PDg0xQV65cgYODA+uupi+j3gFp3rTt27fj7NmzePToETw8PHDu3DnyElJcXIx58+bhl19+YezPM378eNrvsLAwDB48mNWdB0jdO1ZWVqz75KSnp+M///kPvv32W9qus7JWE8odIQsqBkA+QPof//gHBAIBCgsL4ePjQxvM33zzTbLfSFZWFt577z0IBAIMGDAA586dw/vvv0/O/eqrrxj1euvWLVpMjbxLSt46qAgeHh5wcXEhvzdt2kSLqfDz86NNpj3VZVX7GCDVdxsbG7KBpyw2b97MGYwO/DV6mJWVhaNHj7LGzABSq5i+vj55ke6JHgJ9M572FidPnlS46+348eMhEAjw3nvvwcDAAEFBQWQHd2oBhKybSRba2toQCARknydA6v3w9vZGe3s7Ro8eTa6lXlR601/YiJ8sVAoBbm1tpS2vrqqq6rUvTzYQtqqqirNDFxQUKHwWm283LS3thSYhUxdUojW2TltaWtrjTJ5syQLr6upYy97Q0MB6fnt7u0pBbd3d3WhqauJsi7a2tj5PJteTZIi9QUtLC5qbm5WWg+v/zMzMHvULRW8ZyvCy6p3a/bqgoAD5+fmcz2QLcGfT+5KSEs57KMvHlp2dzSCQp0+fxpAhQxj5sihoaWlh6NChtE3QJBIJZsyYQVxcnZ2dMDAwgEAgYF2qXl5eTt4OR4wYQYIgufYZAUA2c+vNSiBAOrHIBuLKE1r5uuytLquC3NxcTiuCOgkIudDXeqjIvcWGnuhhX42nvUV2drbCyT8nJ4dzR28Kvr6+GDlyJGPjwAMHDmDfvn0Kt0fw8fHBli1biIX1RfaXnq/748GDB49XBMre5njw4PFqgicxPHjw4MGDB49XEjyJ4cGDBw8ePHi8kuBJDA8ePHjw4MHjlQRPYnjw4MGDBw8eryR4EsODBw8ePHjweCXBkxgePHjw4MGDxysJtUhMYWGhyqnbZdOCU1BlcyexWMyamKw3uxpmZmZyJjv7q3Dnzp0eydTT69RBeXk5YwOnZ8+evZA9DVTZz6en++jIo6WlhVWPerLZVE/3wT38T5YAACAASURBVCgsLKTtQM22kZaqkM991RtQCSy5wNX/ampqGLrCgwcPHi8LapEYS0tLWrp1RYiNjWXsyKmnp4fr168rTOZUXl6OxYsXM3bDXb9+PS2VvCyioqJou0MGBATQtp4eMWIE4uLiVJJbFTg7OyMoKIh11961a9cqzQArFovx0Ucf4fbt24z/FBEUVa9LS0uDhYUFhEIhjI2NERgYiJMnTyI6OprsAnv9+nUsX76clZhYWlpi9+7dtGMHDhzAhg0bOGU7deoU2UI6Pz8fUVFRjESZrq6ujE2MnJyccOzYMYU7IQ8fPpyxgVRNTQ2Kioo4P9u3b2fkh6mpqcHChQsZG6JFRUVhyZIlKhN0sViMIUOGMBLusaGjowP79+8nu5rGxMRg5cqVkEgkqK+vx8iRI1l3qj5w4ABiYmJI3W3fvp2xc7WnpyfnrrdcYNt4rr29HZ9++imDyMhuGObh4cGaHuPcuXMkPQkPHjx4vGwoJTFHjhwhk7KtrS0t/4cq1wLS3Tmzs7NhYGCA/Px8BAUFYePGjazXVFdXsya/27VrF5KSklivEYvFGDFiBMkK3NXVhZEjRyIvLw/37t1jTMi9BZXCnQ3yCSmbmpqQnp5O+7i6umLOnDmM446OjtDQ0EBzc3OPrwOk1hoqs+ivv/6KtrY2LF26lGYJKy0t5cz0bWJigvb2dhox+vHHHxXmknr48CHGjRsHQLqLLJuVwsXFheTconD69GlcunQJ7e3tnB+2/FhFRUVITU1FTk4OcnJysGfPHhw9epT8zsnJQVFREeM6Dw8PkqFYFitWrKCVT5Flwt3dHevXr4e7uzsj5w8bKioq8MEHH6ClpQVCoRDm5uYApMk5ExISWK/56aefaARTX18fxcXF6OrqIsd9fX0Vbi0ui+7ubjg5ObG24fXr12FiYoLY2Fj4+Pjg5MmTCAkJwdq1a4nOeHt7w8vLi3Htzp07ERoaqpIMfYlHjx7ByMiIsZvo3x0tLS1wc3ODs7PzXy3KS0NP2urvUk99rWe+vr6MXEsSiQQ3b95kTSTZF3hV+4qqUEpivvvuO/L91KlThMTU1dWR7Ydl4efnh+vXr9PepK9evYqYmBgYGhqSfB9c2Vvr6+vx448/Mt7iV61axUliAMDLy4tMPFRW1GfPnuHEiRNobW3lTKDXE5iYmHCSGPlkfl1dXXj06BHZLruyshLjxo3Dn3/+SY7Jfzo6Onp8HSC1xOzbtw91dXUkwdyyZcsYJGblypU0WaOjo2FhYQFbW1sIhUIsXLgQsbGxePjwIaZPn07a08bGhpUYcmUbLykpQWdnJ2tW2tOnT+PgwYOIj49n/cTExGD79u1KWkT1rK2dnZ2orKxEY2MjLl26RNOx69evw9PTE/b29liwYAHrLq/FxcUYMWIEGhsb0d7eDj09PU7LW2JiIrn3kSNHIBQKYWVlhd9++w1OTk44fvw4hEIhvLy8sGjRItp9pk6dyiAxpaWlKC4uxs8//wxAPRJz8OBB1hQdALBu3Try3+XLl+Ho6Ej+8/HxQUREBHx9fVlJzNy5c4mVLCMjA4A0QRxV7r179+Lo0aMqyagqJBIJoqOjIRAIFGa4/zuCyu2jr6//V4uiMsRisVJ3Ixd62lZ/h3p6EXp27do1RkqLtrY2uLu7o1+/fn3yDFn0pgy9afeXCaUkZtKkSeS7nZ0dDh48iIiICNy6dQsXLlxgvFmLxWJcvXqVJKG7ffs2hEIh/vzzT2hrayMiIgKZmZm4c+cOTE1NGe6M2tpaTkuMosRksnkcRo0aRQbRmJgYBAcHY9SoUQpzPagDMzMzlUmMPGxtbRkTT2dnp9Kka+pc9+DBA+zbtw/h4eGEuGhpaSEkJITUi6+vL1asWEG7LiIigpZVd9GiRejs7MTWrVtJcjBASjxkk0xSaGlpIbkvZF1Xzs7OSEpKYiUxDg4O8Pf3R25uLuLi4hAREQEPDw/s27cPO3bswPz583Hs2DGFdQOol3pePjEchatXr+LAgQOc17W1tWH27Nl4+PAh0tLSUFtbi5ycHMybN4+WsZpCZWUlOjo6IBKJIBKJoKuriyVLlkBHRwd+fn4oLy8n/4lEIprl66effqIlHZ0/fz4KCwtRWVmJRYsWAVCdxMTGxnK6Yuvr6/Hvf/8bf/75JwBg48aNKC4uRmVlJe7duwd7e3skJibSSExjYyM8PT0RGRmJUaNGERlHjRqFrq4uzJgxg9zfxcVFLeutOujfv/8rR2IAwNTU9JUiMZcvX2ZkqVYXPWmrv0s9vQw9e/z48QshMRR6Uoa+aPeXAaUkZsKECUhISEBMTAwMDAxoA9Lp06fh7e3NuGbatGkoLS1FaWkp/P39sXPnTjx8+BA6Ojq4dOkSdHV1yf/ygZ2VlZWclhjZrNeAdFLx9PSEj48Phg4dSt5kZYkXIA0oZiNGPYU6lhhZ3Lp1C5999hkt1kEoFOLy5csYNmwYZ6CoutdRJCY5OZlM2FpaWkrdSdHR0QwSc+fOHQbxOHXqFCEMjY2NuHjxImxtbYk5VJaYSiQSEuTNRmJOnTqFoKAgNDQ0EHdYUlISeXt//vw5LVsvF9hITEpKCvbu3QtASo4vXboET09PDB8+HM+fP0dpaSlJ/gcAurq6nBnTm5qasHr1anK+lZUVITwpKSkYMmQITp48yRmEvHfvXtjZ2SE6Ohrm5uaoqqqCmZkZpkyZAldXV0YsFHWOiYkJxGIxwsPD0djYiOrqarVJzOLFi1kD7QFp/f/0008oKiqCWCyGlpYWhEIhdu3aBSMjI5w+fRpJSUmslpiSkhL8/vvvAKT6tG7dOgDAnDlzyDlsba4uKisrce7cObi4uNAI6BtvvIHs7Gxcu3YNly5dor2kSCQSJCYmwtHREUKhkNTvzZs3Sf8RiUTExUdZeSUSCWJjY+Hi4sKa+bkv5DYzM6NNzlyydnd3QygUori4GPfv34ezszOtHSUSCeLj4+Ht7Y3k5GSIRKJeLYBgkzc2NhbvvvsubGxsOPuGKlDWVmx1rmo9URbPBw8eoKSkhLRvbm4uAEAoFOLGjRsvVfa8vDy4ubkhPDyclsgyPz+fFp+ZmJgIJycnhISE0EhMX+phT8og3+7q1jGX/GzHlem5MrCSmM7OTmL2GjNmDHFT2NnZ0UjMoUOHEBkZybh+7ty55Ht8fDxOnjwJQPqWd+PGDRgbG3MK1NTUhLt37zKOl5SUQCKR4MKFCwyFrKyspE3IEydOpP3f0NBAk6m32LJlCxITE1WO3wCA5ORkbN++nfV/efl7e116ejpx85mZmaGjowPLly9HSEgILl68SCwx2tratOuEQiGDxKSlpSE3NxeBgYHkuKWlJSNAVygUwtraGoA0Ff327dsRGRkJKysrLF++HAD7hHbixAlERkbi/v37WLFiBXFX/f7774iMjISdnR0MDAzI+R0dHbh48SIiIiJoLkt9fX2YmJjQjoWGhiI4OJhG8iorK6Grq0t+P3r0CHv27MHDhw9pFgRZUO452Y4lS+QAqfl7zpw5mDx5Mi3upLi4GIaGhkRnIyIiaH7+rKwsfPXVV7S09hSqqqowa9Ys2rFnz56pTWJkXcKyaGpqwunTp6Gvr4+ioiKEhoaSdjU1NcXt27cVkpirV6/C1dUVABAZGUnavy9JTFVVFQYPHozy8nJcv34dn3/+OVnR9cYbb2Djxo3Q19fHoEGDMG7cOPKfkZER/Pz8UFtbCy0tLUKw8/Pz8d5779FcZrt27SITze7du5GWloa7d+/in//8JyM4vC/klp+cuWT19vZGv379sHnzZhw+fBiampqYOXMmuc7ExAQeHh4QiUSYPn06vv76a9jb2/epvA8ePMDHH38MZ2dnZGdn9+jegOK24qpzVespOTkZr732GpKTk9Hd3Y2tW7cSqyAgnbd8fX1fmuzl5eXQ09ODWCyGv78/eSFLT0/HyJEjSZn27dsHGxsbtLa24vfff6eRmL7Sw56WQb7d1a1jLvnZjivTc2VgJTESiYQEycpaFuRJzNq1a2nLRQGpO4mKn9DS0oKVlRUZaJcuXYqHDx+ykpjAwEBEREQgPDwc4eHhEAqFcHFxwbp162jWBz8/P1y7do12rfxkPmLECBw5cgRnz57Fli1bEBISolalKMP69evh5uaGpKQkxmf27Nm0cyUSCQICAnDlyhUA0glFfvWNj48Pg4z09Drgfy0xgHQgvHjxIlasWIGGhgYSG9TV1QUjIyPk5eWR62JjY6Grq0vqetKkScSqsnjxYqSlpQGQBmzLDwqyJCY6Opp8j4+PJ8SIbULbu3cv7ty5g/v372PVqlUQCoWws7Mj7e7o6IiDBw8qbRNV3UnyJAaQulT++9//Yv/+/Yzznz59SlwtqjyPqq/nz58jICAAQUFBNMuUrq4uY3VeXV0dqqurye/Q0FAIhUL4+PhAQ0MDQqEQ165dw5w5c1BeXt5nJKawsBDd3d2ExBw7dgxmZmbIysqChoYGxGKxQhKzfft2ZGZmAgBZDQcoJjE1NTUYO3YsCW5WhqKiIhgZGQGQtpNAICBj0xtvvEGsA5WVlXj77bfh7++PO3fuYPjw4eQe5eXleP3114m1xczMjBDr7u5uYq1LSUnBggULEBYWhrCwMEybNo1WFnWgSG7ZyVmZrF988QWCgoIAAPfv38dbb70FQErm+/XrR6yUZ8+e5Wzn3sr75Zdf9tqtwNVWiupcnXrS1NQkLtOMjAz079+fvHTs3r27V9tSqCt7dHQ0xo4dS/r0zZs3yb22bdsGfX195ObmYtCgQeju7gYgbVuKxPSlHva0DACz3VWtY657KnoWl56rAqXuJHkS4+/vD3d3d/z2228YNWoUq/ny9u3b6O7uxsqVK2lm1F9//RU5OTkKLTFRUVGYOHEixGIxUlJSYGZmRv4Ti8WsrgV5EvPkyRMYGRkhISEBmpqaEIlEjCW6gNRCo2w5NBvS0tJQX19PC5TMyclhXa5cWVlJswTIu7rY5O/NdQCdxERERGDXrl2ExNjb26OtrQ179+6FlZUVzf0RFxfHGhMDSJfSUkq2b98+BhmRJTGxsbE0EmNhYQGAncSsXbsWFRUViI+PR35+PhoaGhAZGYl9+/ahoaEBJSUluH//PiuRkEVvSIyLiwssLCxgaGgIU1NTlQY8Zc/LzMxEYGAgg3h+8sknuHr1Ku2Yg4MDZs+ezYjZio2NxaFDhwBIV3/98ccfKCkpUZvEzJ07lzVImYK+vj6EQiGqq6uRnp4Od3d3Ekzt4ODAiImRvS9VVwsXLiRLshWRmKqqKgwdOpSVMHKhoqIC1tbWOHPmDAQCARlT3njjDZqf/5dffsHRo0dx7NgxxirBYcOGEYvwkydPMGDAAJSVlUEoFJKB+vTp09i0aRMqKyvJpzeBjVxyy07OymQdMmQIIYd5eXlkohOJRBg4cCCioqIAAG5ubliwYEGPZVUkb1+RGLa2UlTn6tRTYGAgRowYAYlEAmtra0yePBnW1taorKxUaSuEvpS9ra0N06ZNw9tvv409e/bQ+vXOnTuhr68PBwcHTJs2jRzPz88nbdvXetiTMgDMdle1jrnuqehZXHquCtQiMadOncLly5cBSIN+qIlFNq6gra0N48aNQ3l5OVauXImoqCgcOHAAz549w8aNG2kkhm2jr+3bt8Pf3x+AlNEtX76cFox64sQJBlmgJvO2tjbi9pg6dSqampposTCyhEUsFmPgwIH44YcflFUBKwwNDTF16lRShqamJhgaGtL8n2wYO3YsI97H39+f053Uk+sePHgAAwMDbN++HcOHD0dKSgohMRkZGVi6dCmrz5HNnURNfrITu6mpKaKjoxnXWltbIy8vDx4eHtDT04NQKISNjQ1Zas9GYrS1tSEWizFlyhSkpqYiNTUV586dg4GBAcLCwnDv3j3Y2dmRe3ChJySmo6MD5ubmRN8AYPXq1SrtvaLseV1dXQyCHxcXh99++w2JiYlkJY8iHDhwgPjPo6KisHv3bhQWFmLevHkAVCcxV65cYbj/ZEFZYiisX7+eBHJbWVnhxo0brCTm0qVLcHR0hIeHB3755RdyvC/dSY8fP8Z3332Huro6iEQihSRm5syZcHFxga2tLT777DPGFgEuLi7k98KFC3Hw4EEcPHiQnOfi4oJJkybRrisrKwMgdQuamZkx9L4ncstOzspkHTp0KBncZSc6AAgPD4eGhga8vLygp6dHa8O+lFd+MlP33gB3Wymqc3XqqbOzEx999BGSkpJw5MgRBAUFYdiwYbCzs6ONdS9D9ufPn6O7uxve3t749NNPyUsHIHVd6uvrw8LCAt988w05np+fj9deew2AYj18WWUAmO2uah1z3VPRs7j0vL6+Hk5OTgrLphaJuXjxItm0i0J3dzfs7OzIbzs7O9y7dw8AyBLeP/74A+bm5khLS6ORmKtXr9Lu1dbWhjlz5sDBwQH19fUMSwwXKisrMWfOHJiamqKjowO3b98m+9DIkhjZ+B2JRILDhw8rXU3EhlOnTmHt2rWMzdEaGhowbtw4hROG/NsEJb8yEqPOdVQMR0NDA9mAjiIxVPBmSEgI4zq2wF42S9X69esZcUtCoZC4D6kl7oA0lokK/pKf0CoqKgg50dTURH19PRISEmBlZYUlS5bA3t4ea9asQVhYmNLJWhUSk56eDj09Pejp6SEoKAh+fn6MXaRbWlpYrV49eZ4sRCIRZsyYQdyv5ubmnHvEAFJ31Pz588lvd3d3ODk5obGxkWyOpyqJkUgkMDY2JrogD1kSExwcTNuOgBqcuJZYU9dPmjQJOjo6pP4oom1sbEwseFS5tmzZwgjS54KNjQ1xBZeVlUEgEKCwsBBtbW20gbmlpQWDBw9GdXU1GQSpTSG7urowePBg2gqymJgYfPTRRySmBwCys7Px2muvwcLCAp2dnSgqKqLFL9XV1WH48OHEythTuU1NTaGnpwcASmUdPHgwYmJiAEjfUAUCAZkEdHR0aKsG5dFX8g4ePBiRkZE0a4A69wbA2VaK6lydeqLOHzRoEAoLC9HV1YV//etfrHuvvGjZIyMjyQv+o0eP8Omnn5J77dixA3p6erh9+zYEAgFZXZqVlQWBQICuri6levgyygCAtd1VqWOueyp7FpeeW1hYMHiHLJSSmJEjRyrcVvzOnTtYtWoVAOmAd/nyZRQWFqK8vJxMsGVlZSSuITc3F5s2bQIAxl4je/bsQUFBAdrb22FiYoIVK1Zgw4YNrKZw2bfcR48e4R//+Afy8vKQl5cHTU1NNDY2ApCavKlzDx8+TLuHu7u72lHrdnZ2CicOKnCQLVATAGuArmxdcUGd6+7du4cdO3bQjlGkwNHREU1NTVi+fDkWL16MwMBAsiooMjKSERNDuQjKy8uRkZGBkpISfPPNNwxLjqenJ8aPH8/qUqOsVY6OjjQS4+joiLq6OkgkEmhqaqK6uhqHDh1CfHw8cXPZ29sjODiYk41HR0cjJiYGWlpajE2kZFFRUYERI0agrKwM3d3dOHToEMzMzBAVFYXs7GwUFBTgyZMnKCwsxMOHDyEUChVu7mdjY0ObABWhra0N2tra8PHxIcfEYjG2bNkCbW1tJCYmMgixmZkZUlNTie5v3ryZ6Cqlz5cuXVL6lkKhs7MTlpaWtBgoChs3bkRBQQHu3r3L2JWXWvp99uxZmvwUbG1tiSn51KlTSE5OppnJXVxcSFwXII3Dee2111ReOvvgwQO88847mDdvHtzd3fHBBx9AR0cHbW1tWLt2LX755RecP38eBgYGtJ2sz549i6+//hru7u4wNTWlESlASux++OEH2q7EgHQH6f79+6N///6YMmUK4/+oqChabIa6cmdlZeHHH3/EmDFjyETHJatQKMSAAQPIRmVHjhyBQCAg1vDJkyejX79+eP/99/Hxxx9j8uTJDAtfb+Vta2vDxo0bMWTIEAaJVfXeABS2FVudFxYWqlxPFDIzM2l7X+3cuZN14cmLlj0yMhJjx45FSEgITpw4QaxFBQUFmDhxIsaMGYPc3Fzs378f7777LtasWYPNmzdjwIABsLOzg0QiUaqHL7oMAFjbXdU65ron23Flep6Xl6dw00OlJMbW1hYzZszAypUrWT8aGhrYs2cPuru7yYD79OlTTJ48GceOHUNjYyNsbGxIANPz588xb948LF26FHPnziV5a8LCwhgBjxkZGdizZw80NDQwZswYDB06FJ988gn09fUZ+W5u376N4OBgHD9+nBZLcvLkScybNw9aWlqYPHkyeYttbm5WujeLLDo7O+Hq6qqSG8Dd3Z3TD/v999+T793d3fDw8MDYsWOVWpzUuS45OZkRd7Ru3TqaIkgkEvj5+cHBwYGQ1PDwcLi5uZFzDhw4QP6TSCQIDQ3FwIEDWXe8FYlErFvaA1K34Lp16/Dmm28iPT0dgLT+KWtQZ2cnzeRK4fnz5/Dw8MDgwYOJ718era2tOHz4MN555x3OzdwoyC/nz8/Ph5WVFdatW4cFCxZg5syZ0NDQwNSpU2luBjacPHlSqRWkpKQEZ8+exc6dOzn3aAgJCcF3332HhQsXori4GGKxGM7OzqQvlJaWQltbG99++y3pX2lpaVi9ejXee+89tWMV2EjMH3/8gaCgINb+8OzZMxw5cgRvvfUWLS6pqKiIc9mtrJ4VFRWRFwoKjx8/VivfUmtrKzlfPv9Vc3MzCVCWR1NTE3Jycjjjgdji5KhnFBUVcba/InKrqtzqyiqP5uZmHD58GK2trXj69CmysrIQFRUFU1PTPpdXIpFw5ulS9d6UzFxtpazOKSirJ1nLamNjo8I6f1Gyd3R0QCwWo7CwUOmO3jU1NaiurkZHRwcj7kWVOnmR9c/V7qrWMZf8qra1LLj6KvACs1hTSsa29TvbuYqEpNDd3Y2mpibWt/2CggKVtoDvKTo6OlgbX12wTbRpaWlKEyGqc11VVRXDdaCK7KokI+xJskQKsvFCsh1BLBYrTGbI5QaRhSKz+otAdna2wgmnqqoKd+/eVTnBI9U+zc3NjGtaWloYzxKLxaw5tHqCgoICpQOKbBuIxWLGqkQeLxfHjx+Hrq4uzYIXHx//l6SA4MHjr8QLIzE8ePDgwePFoKioCMuWLcMXX3yBCRMmYPny5bh+/fpfLRYPHi8dPInhwYMHj1cYvdkDhQePVx08ieHBgwcPHjx4vJLgSQwPHjx48ODB45UET2J48ODBgwcPHq8keBLDgwcPHjx48HglwZMYHjx48ODBg8crCZ7E8ODBgwcPHjxeSSglMRKJBDdv3mTNQaEqfH19FW4J/zJQV1cHR0dHrF27FgEBAf/nn6sq7O3tFeal4PHqgK2f9UX/5fHiUFhYiF27diE1NfX/axleFF4F/X8VZHyZUJcvKCUxbW1tcHd3Vys1tjyuXbsGPz+/Hl/fW0gkEmhoaKChoQF3797Fp59+qvJOqq/ic9XB2bNnSSoAHn9PiMVixpbkbGDrZ33Rf3lIoWo7qHOf1NRUvPPOO5w5fl4GXoQMfVVXvcWroP+vgowvE7LjmCp6pJI76fHjx690Befk5ODjjz8mv1/W5lB/1XN5/N/C5cuX1c6RJItXvf/+XdDbduC6D5Ut+K9EX8vQV3XVF3gV9P9VkPGvgCp6pJDEJCYmwsnJCSEhIaSCb9y4AaFQiKamJpKBMiEhAYA0/8uNGzdQXFyMoKAgkisnPz8fcXFx6O7uhlAoRHFxMe7fvw9nZ2daNmSJRIL4+Hh4e3sjOTkZIpFIYQIvVVBcXAxbW1t8+OGHtMzEEokEiYmJcHR0hFAoJASDqwx98Vyue3PJIpFIEBcXh4KCAmRnZ+PMmTMkcVZERATc3NwU5hxSBffv30dWVhaam5tx7do1tLS0wN3dvc+sM6q0eWxsLFxcXPDo0SNyvLKyEufOnYOLiwtKS0vJvfqibWSfzaUDimTu6+cp6lOxsbF49913ackW8/Ly4ObmhvDwcFodUP2MAlv/peRgq/O+LtfNmzdJRnSRSISWlhYIhUIkJSVxytGXbUz1n9raWkRFReH8+fO0ZJRsOgaw1y9bO/QEbPcZOnQoIiMjkZ6eDhcXF/Lmqe54oUiPqOu4xlcuGfqqjFwyJyYmQigU4sGDBygpKSH6kpubC0CayZvK3q7umMCl/1z9pydQNh8qG0fU6aN92TcUya2s3/QE6rQzNY6p2uc4Scy+fftgY2OD1tZW/P7776SCq6ur8cYbbyA1NRUSiQT+/v748MMPAUhZ0+uvvw5DQ0NoaWnBwMAA6enpGDlyJPT19eHt7Y1+/fph8+bNOHz4MDQ1NTFz5kzyTBMTE3h4eEAkEmH69On4+uuvYW9v36vKKy0thaOjIz766CMkJSWRRHZGRkbw8/NDbW0ttLS0iD+SrQx99Vyue3PJ4u/vjwEDBmDnzp2wsbGBjo4OBg0aBEdHR9ja2mLq1KlYs2ZNj+vm3r17+Oyzz3Du3Dls2LABQ4cOxd69e3Hw4EEMHDhQ5Yy6iqCszXfv3o20tDTcvXsX//znPxEWFoaqqioMHjwY5eXluH79Oj7//HN0dXX1WdtQ4Kp3ZTL39fMU9akHDx7g448/hrOzM7Kzs1FeXg49PT2IxWL4+/vj6NGjAEDrZwB3/wXY6/xFlCs/Px/vvfceHB0dybm7du0igy+bHH3VxhKJBC4uLhAIBDAzM8P69evx5ZdfYvTo0ejq6uLUMa76lW+HnoLtPkOHDoWxsTEsLS0xd+5czJo1CwD3WNQTPQIUj69cMvRVGblkTk5OxmuvvYbk5GR0d3dj69atGDVqFHG729nZwdfXV+0xgUv/udq3p1BU58rGEXX7aF+Of1xyK+s3PYWq7Sw7jqna51hJTG5uLgYNGkQy696/f59WwR9++CEJAktNTaV1lC+//BKXL1+m3W/btm1kcP3iiy8QFBRE7vvWW28BkGaJ7tevH/Lz8wFIYzW+GdZV+gAAIABJREFU++47NaqJG/Hx8fjss8/I7zt37mD48OHkd3l5OV5//XXyhshWhr54Ltu9lckybNgwEhBcW1sLgUBAMoOHh4dj8ODBvZJx0aJF8PLyQlJSEr766ivS5l9++SX+/PPPXt2bAlebp6SkYMGCBQgLC0NYWBimTZuGOXPmoKioCEZGRgCkKdgFAgHKy8uJXH3RNsrqnUvmF/U8ZX2KMqlGR0dj7NixqK6uBiC1dlCg+pmi/stV5y+qXGZmZli+fDkA6Zvk3r17lcrRV20MAG+99Raio6MBSLN1v/766wgICODUMUX1K9sOvYH8fYYOHYqrV68C+N/4FNlz1RkvuPRI2fiqSIbellGZzJqamrC0tAQAZGRkoH///sRisXv3bkgkErXGBEX6r6h9ewpFfZdrHOlpH+3LvqFIbq5+0xuo0s4AnS+o0udYSYyDgwOmTZtGfufn56tMYoYMGQKhUEi7386dO4lQsv/n5eWR+4pEIgwcOBBRUVEAADc3NyxYsECh8KpCnkwcO3YMP/30E+2cYcOG4eTJk5xl6Ivnst1bmSwjRowgb8rt7e0QCASorKzkvL+6WLp0Kby8vJCcnIwxY8aQ4yNGjOgzlxJXm58+fRqbNm1CZWUl+VBm7IqKClhbW+PMmTMQCATEfNxXbaOODsjK/KKepyqJaWtrw7Rp0/D2229jz549aG1tJedR/UxR/1VU5y+iXE+ePMGAAQNQVlYGoVBIyqFIjr5qYwB4++23kZmZSX7/8MMPOH78OAB2HVNUvy+SxFDxKNnZ2TRdU3e84NIjZeOrIhl6W0ZlMgcGBmLEiBGQSCSwtrbG5MmTYW1tjcrKSjg4OJBrVB0TFOm/ovbtKVSdD2XHkZ720b7sG4rkVtRvegpV21mWL/SYxFhYWOCbb74hv/Pz8/Haa6+R3x9++CHu3bsHQFr4Dz74gPw3dOhQRiXv2rWLCCX7vzw5Cg8Ph4aGBry8vKCnp0csDhSKi4thZmZGGKKqkJ/sbW1t8dlnn9ECbX/88Ue4uLiwlqGvnst2b2WyjBw5UiUS01MZly1bBi8vL6SkpNBIzMiRIxkkpqfP4GpzFxcXTJo0iVb2srIyPH78GN999x3q6uogEoloAxabfvVELnV0QF5PX8TzFPUp2Y78/PlzdHd3w9vbG59++ikWLVpEzqP6maL+y1XnL6pcALBw4UIcPHgQBw8eJOcpkqOv2hiQDsaycT+zZs3C+fPnOXVMUf3KD6g9lUkRicnJyaHpmrrjhSI9UjS+csnQF2VUJnNnZydxux85cgRBQUEYNmwY7OzsyJu6OmOCIv1X1L49Lauq86HsONLTPtqXfUOR3Fz9pjfPU6WdATpf6DGJuX37NgQCARITEwEAWVlZEAgExH/15Zdf4vr16wCkfr933nmHVPiQIUMI26ewY8cO6OnpAZBGwcfExACQMlOBQECu1dHRQVVVlUKB6+rqMHz4cFhbWys8TxY3btzAv//9b/KbUqbbt28DALq6ujB48GBUVFRwlqEvnst2b2WyfPPNNwgNDQUg7YACgYD8FxcXh08//bRXMi5ZsgSXLl3C7du3MXr0aHJ8xIgRuH//PuP8njyDq82zs7Px2muvwcLCAp2dnSgqKoKzszNsbGyI77isrAwCgQCFhYVoa2tjbZueyKWs3hXp6Yt4nqI+Ra0cqampQWRkJHHzPXr0iNb+VD9T1H+56vxFlQsAYmJi8NFHH8HV1ZUcUyRHX7UxIB2MqX2QRCIRvvrqK5SWlnLqWEhICGf9yrZDb2SSv8/gwYMRERFB6kVW19QdLxTpkaLxVZEMvS2jKjpiamqKQYMGobCwEF1dXfjXv/5F2zdFnTFBkf4r6j89Lauyvss2jvS0j/Zl31AkN1e/6c3zAOXtDDD5gnyfkwdnYO/+/fvx7rvvYs2aNdi8eTMGDBgAOzs7SCQSHDp0CJ988glWrVoFZ2dnvPnmmzh79iyioqLw1ltvQVtbmzDHgoICTJw4EWPGjMH58+cxYMAAGBkZoaSkBEeOHIFAICA+vsmTJ6Nfv354//338fHHH2Py5MnIyMhgyBYVFUXzsSpCaWkp1q5dS55DMb6zZ8/i66+/hru7O0xNTYnfkq0MffVcrntzyRIXF4d//OMf0NfXR2VlJTGj2traoqKiAps3b0b//v0RHh7eIxlzc3MxZMgQaGtrY+vWrfif//kfJCcn49atW3jnnXewf/9+1mAudZ4hFAoVtrmTkxP69++P/v37Y8qUKWhqasKDBw/wzjvvYN68eXB3d8cHH3wAHR0dXLt2jbNt1JUL4K53ZTL39fMAcPYpANi4cSOGDBkCLy8vREZGYuzYsQgJCcGJEyfI26xsP8vNzVXYf9nq/EWVC5AG2f7www+M57DJoaj/9US2t99+Gzo6OoiJiYGpqSm8vLwAgFPHQkJCWOsXoLdDb2SSb88333wThoaGePr0KY4ePQqBQIArV66oPV4AivWIa3xVJENflFGZzACQmZmJlStXkt87d+6kLflWd0zg0v+IiAjO9u1pWbnqXNk4om4f7eu+oUhXuPpNb54HKG9n+XGMq8/JQuES65qaGlRXV6Ojo4PGhCQSCZ48eYKuri60tbWhvr5erYKwobm5GYcPH0ZrayuePn2KrKwsREVFwdTUlPV8aql0b9DU1IScnBy1VuH0xXP7ShYuvCgZX9QzWlpayNJxCq2trXj+/Dn5X9Wl9urK1dt676vnKepTEomELKXv6OiAWCxGYWEh6urqFD6Lq/8C7HX+IspFgWuMUCZHb2V7++23kZ6ejvz8fKJPFNh0TFH9yrZDb2RSdB9Voa4eqTu+sqG3ZVSmIw0NDeR7Y2Mjo8+rOyaw6b+q/UfdsvZ0PuxNH32RcivqNz19HgVl7Swvo7K+8rfJnXT8+HHo6uqio6ODHIuPjyeuFB48ePBQF2+99Vaf7IXzqoMfX3mog1ep3/xtSExRURGWLVuGL774AhMmTMDy5cuJv44HDx481IFIJIKHhwcEAgF2797dJ9biVxn8+MpDFbyK/eZvQ2JkwW/Pz4MHDx4vBvz4yuP/Ev6WJIYHDx48ePDgwUMZeBLDgwcPHjx48HglwZMYHjx48ODBg8crCZ7E8ODBgwcPHjxeSfAkhgcPHjx48ODxSuL/JIkpLCxEVlYW+a1OCvHGxsZePVvVTdlkIZFISEZXRXj69Cltq24AuHbtmsLVBvL5p1RFfX09ybAqC7ZjqkJedgrV1dU9TvNeXl7O2Izp2bNnEIlEnNeIxWIUFBTQjpWVldH20FAHPZG/paWFlmDtRaGqqoqWl6QnUOf6lpYWpKWl9ep5PHjw4KEqekxiUlNT+3yp3qlTp/D06VMA0hwhUVFREAqFtI+rqys8PT1p13V0dGD//v0k10NMTAxWrlwJiUSC+vp6jBw5EoWFhSrJMG7cOCQmJiIqKkphyvPU1FRs3boVkZGRtI+5uTlsbW0Z53t4eOD69esICgqipR2nyjp9+nRaedggkUigqalJUtDfuXMHRkZG6Ozs5CRP48aNI0m+HBwckJSURD7R0dGwsLBgve78+fMwNDRkHNfW1qbl2lEHn3/+OeuE2NjYiMWLF6O2thbd3d0QiUSMT1RUFGveF0tLS+zevZt27MCBA9iwYQOnHF1dXRg2bBja2toASEnN+++/j7i4OKxevRolJSWc1z569Ajbtm3D/v37ybElS5bQ8gKpgqqqKgwaNIhzN8q6ujrs3r0bzc3NnHUiEong5OREy3FVVVWFEydOEBLn6ekJAwMDRvm9vb1haGjI2Ydlj48ZM4bkeFEFGzZsgLu7O+M4pbc8ePDg0VdgJTGFhYXYtWsXSdPNhtLSUhw5cqTHb69sePjwIcaNGwdAmjK9paWFcY6LiwvOnDnDOF5RUYEPPvgALS0tEAqFMDc3BwCYmZkhISFBZRlmzZpFJoWxY8eiurqa9byMjAyYmZkxjgcHB7MO4Hp6esjPz4eHhweMjY3x7NkzREdHIyQkBHp6ejh27BgiIyMREhICIyMjUq9paWkIDAwkJGnTpk1wc3NDWFgYDh48SI6vX78e9vb2tGdevXqVRqhmz56NlpYW8rl37x42bdrEWr5Lly7Bzs6OduzQoUPQ0tJiJSIXLlyAj48PIiMjsWfPHpL0TBYTJ05krcf79+/D2toa7u7uuHv3Lq5du8Ygr0uXLoWfnx/jehMTE7S3tzMy5CrbDnvu3Lnk++3bt7F8+XIAwJUrV7Bz504AoBHfqKgoODg44Mcff0RkZCTRzdTUVHIsMjISx48fp+UC4UJtbS00NDQUnuPp6Ym1a9eioKAAAQEBEAqFWLhwIfT09Gh18+DBA9p1R48ehbm5OWpqaqCtrY20tDScP38efn5+2LlzJ5ycnBAQEIDMzExCfqOiomhkfNmyZYiOjkZtbS3pk/IQi8Xw9vZGdHQ0kcXa2hrOzs6wsrKCtbU1OR4QEIDFixcz8ifx4MGDR2/ASmJSU1PxzjvvKB2Mi4qKYGRk1KcWmZycHNbjJSUl6OzshIuLC4KDg8nxxMREMlAeOXIEQqEQVlZW+O233+Dk5ITjx49DKBTCy8sLixYtIm/fAGBvb4+YmBjahPDLL7/g1KlTEAqF8PDwwOnTp2FgYMAoY0ZGBv744w/k5OTQPk5OTnBzc2PIv2nTJuTn52PTpk3o6urCsmXL8OzZM4jFYixcuJCzPrq6uhjP/vnnn9Ha2kp+s03YjY2N2Lt3L7q7u5GdnQ0AmD9/Pu2czMxMViIGAD4+PjQSY2FhgdOnT3PKefToUZIyfd++fazEcdKkSRCLxbQU7levXoWJiQmeP3+uMEeHsbExjVRTViRbW1syucfGxuLhw4eYPn06mYxtbGyIpebZs2eknX/88UdcvHgRx48fx/bt22FlZQWhUIhdu3YhJCSEkEt5wqarq0uyuba1tcHQ0JBs3V5aWopJkyaxJmeTR319PY3EdHd3M1Lbd3R0QCgUkt/BwcFYunQpTR8oK5ssmpqacPPmTYSHh2P+/PlIT09Heno6rl27hn379imVDZBmPK6ursaFCxfg5OREjnd2diI2NpZxfklJCSQSCTQ0NIiVSBlJ48GDB4/egtOdRKXAVoZTp07hwoULfSZQS0sLRCIRvLy8aG94zs7OSEpKYpCYyspKdHR0kIFTV1cXS5YsgY6ODvz8/FBeXk4zv8tOAJMmTUJRURHt//379yMgIIBhtpdHWloa9uzZg6KiItonJCQEcXFxjPMNDQ0RFRVFJip/f3+Ulpbi/PnzWLJkCc3iUFxcTLs2Ozsb/v7+EAqFOHv2LGbPnk3Oj4qKgoGBAe0aiUQCb29viEQihIaGYubMmRCLxZg5cybtbdvV1ZWTxFy5cgV2dnZobW2FhYUF421fHsePH6eRGG9vb/j6+tKeN3LkSFy9ehU2Njaora0FAJJNFgBiY2Nx8uRJcv7ixYtJfIU8iYmIiCDXAcCiRYvQ2dmJrVu30txOp0+fJtYokUiExsZGiEQizJ8/HyKRCO3t7fj4449JfIqmpqbCeBqKxPj4+CApKQkikQh6enpISEjAkiVLiDuUQkBAAMOyFBUVhWnTpmH06NE0S8WhQ4cYlj/KUuLt7Y3Tp0/j9u3b8PHxAQAEBgZi9uzZqKysBACkpKTg4cOHAKSZuBMTE2FtbU3cgunp6TQSEx8fz1lWHR0d1NTUQFNTE6GhoYiMjMSFCxewYMEC0j8odHZ2YuzYsYiOjmaQmMePH8PJyanXcTk8ePDgwQZOEjN06FBERkYiPT0dLi4ujAybFJqamvD999/3SojGxkZcvHgRtra2WL9+PQDQBkmJREIGQXkSI4u9e/fCzs4O0dHRMDc3R1VVFczMzDBlyhS4uroyLBpTpkxhxFm4u7sTt8zz589hYGBAs95QZT537hwtvkT24+rqygjUNTQ0RExMDJnoAakFxdbWFvr6+uTYsmXLSJbXzs5O1NTU0D4rV67EjRs3GMdzc3Nx8OBBRhlnzZpFCI6mpibtv8zMTGzbto21Lv38/GBra4vg4GDWoNWGhgZaPIeFhQWNxKSmpjKumzx5MuM+oaGhhIzExMQwiAlVF2yWGPlz79y5w9CNU6dOsbr3NDU10draChsbG4wZM4ZBYh48eIDk5GQAUtdPcHAwhEIh/vOf/8DX1xdCoZDo5PXr16Gnp6c0qzQFZ2dnzJs3j9NSUVtbi0uXLsHDwwOTJk1CamoqCgoK0NDQgHnz5mHDhg24cOECzp8/T7uuu7sb06dPR3x8PIKCgrBhwwaUlpZi7dq1kEgkePToEXR0dCAUCqGpqQkvLy/iMnvy5AktaFtHRwc3b97EunXryDm3bt1iteTs2bMHAQEBAMBqiamtrcX06dMVxpjx4MGDR0+gkMQYGxvD0tISc+fOxaxZszhv8tVXX/VoVY48KJ86AISFhWH79u2IjIyElZUViVlgIzHFxcUwNDTEjRs3AEjf0mWDT7OysvDVV18hIiKCdt3EiRMREBCAyMhInD9/Hra2trh16xb09PQASK0Rc+fOZUzGtbW1mDVrFtrb29He3o6HDx+isLCQ/La2tsalS5do1xgaGpKYmMuXL+PIkSPYu3cv6uvrsWDBAvJGPnXqVDJxP3/+HNevX0dqaipSU1Nha2uLtWvXkt/UJyUlhXyXJVz+/v5YvHgx+U1ZYqgyu7q6wsjIiFYuT09PREZGYt++fdDX10dkZCRcXFwYAczHjx+n1fGJEydgaWmJyMhIaGtrIzU1FYmJiTh69Cg5RxmJiY+Px9GjR0k9Lly4kJPECIVCBolJS0tDbm4uAgMDyXFLS0tGIHhjYyOGDBkCZ2dnWFtbY9u2bXB1dUVkZCQmTJiA8PBwhIaG0lw5APDnn39CIBAQPWpuboaTkxPc3d2xa9cueHl5Mconj+rqanz//fcoLi5W6m7JycnBli1bAAAFBQUwMDBAaWkpPD09MWfOHFa3zu3bt+Hp6QlbW1tSlp9//hlGRkbw8PDAwYMHATAJrbm5OY4dO0b0cPv27bC3t0ddXR0uXrwIADh37hy8vb1p1wUHByMsLIz8licx3d3dyMrKQllZGcaOHau0fnjw4MFDHSgkMVevXgXwvzEyXBgxYoRCE7yqkCUx0dHR5Ht8fDyZsGRJzPPnzxEQEICgoCDa83V1dWkrNgDpag95U/0PP/xArvPw8ICTkxNaW1sxfvx4ANLYk5SUFIacTU1NmDNnDvnt4OBAIwN2dnbkzZTC5s2bkZ+fj87OTvz6668IDg6Gr68v6uvroaurS4JtFy9eTCZuWZSUlGDIkCEwNzdnBL1OnjyZEUtUU1MDU1NTLFu2jByjJi5qUqLiZtiCLQMCAkhMTH5+Pon74AKbJQYAFi5cSAgdG4kJCwsjbXvz5k1s2rSJWLSmTp1K4mTkSUxsbCx0dXVJHUyaNIm0pawb6siRI/D19QUgdT3u3r0b8+bNw7x585CVlYWKigps27ZNJXeSoaEhxo8fDzc3N5iamuLOnTvo7Owk5LO0tBQtLS0IDw9nvZ6Kf0pLS0NDQ4NKJMbY2BiJiYkIDQ1Fd3c3QkNDsXnzZmhrayMiIgIbNmxgBNe7ubmhtbUVtbW1aG9vx9q1awFILSnUajR5EnPs2DEaGQH+d7sBKkh+48aNtPiroqIiZGVlobq6GmFhYXBwcIC9vT1pExcXF2zcuBETJ05Ee3t7j5fR8+DBgwcXlLqTAGlMRr9+/VjPa2trY7iTGhoaGC4YVSBLYmJjY2kkhhp8ZUlMZmYmbeUO9fnkk09w9epV2jEHBwfMnj2bFhArO6k6ODgQ0jZjxgx4e3vTiIos5EkMZTGiwEZiDA0N8fjxY7S3t2PChAmExNTW1mLJkiVISUlBSkoKpk+fziAxRUVFMDQ0xJkzZ1jf9uUnJJFIBBcXFzQ0NLCSGFtbW7S1teHAgQOwtLQk8SmykCUxALB//36FS4+5SMydO3cIoVRmiYmLi2NYV6j2kicxbOd2dnYCkFoMgoKCiCyUvtTV1eHw4cMQi8W0Otu2bRuCg4ORmZmJadOmISMjAxEREdi2bRtxz+Xm5sLS0hIrVqxAaWkpCd6+e/cuZsyYQdrv6NGjmDp1KuuqvV27dpGYKHVIDCAN8D558iQJsqUsbPb29jRrFyDVRwrPnz/HjBkzEBISgnPnzhG55XXmxIkTDBJD4fDhw6ioqODsDxQ6OjpoKwdTUlIwZcoUzmXkPHjw4NFbqERicnJyOEmMi4sLbbITi8UYOHAgfvjhB7WFoUhMXl4ePDw8yFJSGxsbHDlyhDyPmpS6uroYbqy4uDj89ttvSExMREZGBuezqD1XKBw6dAg3b94EIF2ZM2DAALJhXnNzM22ir6+vpw3oGhoatP/t7OwY/n9DQ0McPnwYX3zxBdzd3QmJefbsGWdMDCBdfWVvbw+RSAQfHx/s3r2b1RIji9raWojFYrS0tLCSmCdPnmDNmjUKgy3lSUxraysWL16M3Nxc1vPlA3vZVs0oIzHR0dEM6wplJVLFnUTVm2xckKmpKWPVD0CfxE1MTJCQkIDKykrMnDkTZWVlqKysRGVlJdEvOzs7iEQiQmIA6bLkM2fO4OLFi/Dz80N9fT1++uknEmgrCwsLC1y7do38VofEJCQkwNXVlax6Ki0txapVqwBICSu1cV9eXh6WLl2KAwcOwM/PD0ZGRjA1NSXnnjhxguz3oozESCQSYt0rKirCuHHjVNoLJyUlBfb29nj48CHmzJmjME7I3t4elpaWOHv2rNL78uDBgwcbFK5Oonz/2dnZEAgEjKDR4uJiaGlp0d46JRIJDh8+zDphKYNQKISWlhasrKxw69YtMvmUlJSQyVNRYK9IJMKMGTMI+TA3N+fcIyY7Oxvm5ubIysrChQsXMHHiRLKjrImJCUaPHo3Dhw8DAIKCglBfX0+ubWlpIQTv3r170NbWRlZWFnx9fVFdXY2CggKGi8bAwAD5+fmEOAQFBcHLywsVFRXQ19eHRCLBmTNnMGrUqP/X3vmFNPWGcdw7bwXxwpuuEqW0BiES5M3UbgyHWrguRBuiWBooswsVwdVKB160Ssw/NEhFVEqUMqibxH+zEDOLbFNz5DT/bTnnNHPf38U4L+fdOSdnv26E5wPenLlz3vPynp3v+7zP831ZguWXL184r5L29nYYDAZJTsyFCxdk71FJxACBZMzgvB0xYhGzu7uLsbExrKysIDU1FZWVlZLZtcFg4HJihKRYMXJjQlydNDQ0xL1IGxsb2dgKJbFXLvp37do1WK1WyfFgEfOn5aSlpSUWEcrOzsbi4iImJyfZ0orX68WjR49YObOYg4MDNDQ0SDyXDhMx379/R1lZGXQ6HXNd/vHjB8xmM86ePasYNTEajejo6EBbWxvsdju3nJSZmcmiVcEi5u7duxgYGMDy8jLu37+PlpYWlsxvs9lw8uRJ1NfXK7ZXTHl5OSIjI/9oMLm3t4e6ujoAgMlkCum8BEEQwciKmMHBQYSHh6O4uBiLi4u4ffs2wsLC0NXVxf5nfn4etbW1soZ0LS0tLMn2KFgsFpw7d042J0FYTzebzbIixuv1QqvVsvJTIBAVKikpgVarxdu3bzmxZTKZWDlsZ2cnKioq4Pf7YTAY8OrVK+zu7kKj0eDy5cu4cuWKbHs/ffoEtVrNfuxnZmaQkZGB5ORkyey/sLAQNpsNADA5OYlbt25hYGAAzc3NbMbr9XoxNzeHzc1N2eWIp0+f4vHjx3C5XNyfWq2WbZ/b7eZEjFqtRlNTE8xmM1wuF7RaLbKysmTLp9vb23Hz5k1UVVXBbDYzgbC/v4+amhpERERAo9Gw6MDDhw/x9etXAIGlQCGfQihjPjg4QGJiouQ6z58/h9FolG0/EBDKHo8HBQUFnJ394OCgYtRmaWkJU1NTcDgciIuL4yJOq6urcDqdnGdOaWmpJLG3r68PKSkpEpdZjUYj2crh5cuXyMnJwezsLHf858+fsFgsXGWf0I8rKyucS7OYubk5xMfHM3uAqqoqGI1GjI6OYnl5GePj4ywKubGxgdnZWVZld/36dc4UcGdnB7m5uZienkZbWxuAwEQj2DOovLwcZ86cQWFhIbc9xPv373Hjxg1sbW1Bp9MhNzdXUZzY7XaUlpbCZDJhfHwcqampqK6ulvSLQE1NDZqamjgxShAEcRT+etsBwUAtmK2trSNZlIvZ39+XeKQIjI2NIS8vD+Hh4dxs1+FwoLm5GRUVFYpGeX19fVCpVLh06RK+ffuG7e1tSWh8dXUVra2tTGgIdHR0ICYmhgmeX79+4fXr1zAajbh3757s7L+zsxPR0dHcMZ1Ox2bugsssEJjl6vV6pKen4/z584iNjUVRUZGsOHzy5An0er0kB0jJUXVtbY0TMSqVSpKrI/RdcNhf6DOlPnU6nYpjQIzP50NPTw9Onz6NzMxMyefd3d2SnA4xnz9/RlpamsSi/8WLF5ypYHV1NUsC9vv96O/vR0REBPLz87nzvXv3DgkJCaxKBwi8+EPdxyg9PR02mw0OhwOtra2orKxEd3e3rOGj3FLKzMwM9Ho9oqKioNfrFa8T7Dfz4cMH3LlzB1evXoVarUZSUhJUKhUSEhJQVlbGxmFjYyOXn+VyuZCTk8OJe5/PJxFQIyMj3HPrdruZy6+A3+9HfX09Tp06xZ6TjY0NPHv2DA8ePIDFYuGeB7/fj97eXly8eBEnTpxAVlYWE7ri9hEEQfwtx24DSMHMCwjMZq1Wa8hVD8Iyzfr6uuSlE2oi8vDwsOJGhmKCk2BD+c5hTE9Py55HKeq1s7PDibKjVocISw//AqfTKVt15fF4DnV83t7eloi6UO5FLjdFOC6+5sePH0PuG7vdjoWFBVitVtn7CZU3b978r800Q8Xn80mEw+/fvxW30wACz8LExISibYLQbrfbjYmJiZD6wePxsFwigiCIf8WeM4zXAAAANElEQVSxEzEEQRAEQRAAiRiCIAiCII4pJGIIgiAIgjiWkIghCIIgCOJYQiKGIAiCIIhjyX/0uB5G7yd6xAAAAABJRU5ErkJggg=="
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![%E5%9B%BE%E7%89%87.png](attachment:%E5%9B%BE%E7%89%87.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "没有正确的方式来分配标记，只有根据目标不同或多或少有用的方法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
